{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/02_pytorch_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "[View Source Code](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/02_pytorch_classification.ipynb) | [View Slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/02_pytorch_classification.pdf) | [Watch Video Walkthrough](https://youtu.be/Z_ikDlimN6A?t=30691) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r8C1WSzsHC7x"
   },
   "source": [
    "# 02. PyTorch Neural Network Classification\n",
    "\n",
    "## What is a classification problem?\n",
    "\n",
    "A [classification problem](https://en.wikipedia.org/wiki/Statistical_classification) involves predicting whether something is one thing or another.\n",
    "\n",
    "For example, you might want to:\n",
    "\n",
    "| Problem type | What is it? | Example |\n",
    "| ----- | ----- | ----- |\n",
    "| **Binary classification** | Target can be one of two options, e.g. yes or no | Predict whether or not someone has heart disease based on their health parameters. |\n",
    "| **Multi-class classification** | Target can be one of more than two options | Decide whether a photo is of food, a person or a dog. |\n",
    "| **Multi-label classification** | Target can be assigned more than one option | Predict what categories should be assigned to a Wikipedia article (e.g. mathematics, science & philosophy). |\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-different-classification-problems.png\" alt=\"various different classification in machine learning such as binary classification, multiclass classification and multilabel classification\" width=900/>\n",
    "</div>\n",
    "    \n",
    "Classification, along with regression (predicting a number, covered in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/)) is one of the most common types of machine learning problems.\n",
    "\n",
    "In this notebook, we're going to work through a couple of different classification problems with PyTorch. \n",
    "\n",
    "In other words, taking a set of inputs and predicting what class those set of inputs belong to.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9wTlTaDKH7Oj"
   },
   "source": [
    "## What we're going to cover\n",
    "\n",
    "In this notebook we're going to reiterate over the PyTorch workflow we covered in [01. PyTorch Workflow](https://www.learnpytorch.io/02_pytorch_classification/).\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/01_a_pytorch_workflow.png\" alt=\"a pytorch workflow flowchart\" width=900/>\n",
    "\n",
    "Except instead of trying to predict a straight line (predicting a number, also called a regression problem), we'll be working on a **classification problem**.\n",
    "\n",
    "Specifically, we're going to cover:\n",
    "\n",
    "| **Topic** | **Contents** |\n",
    "| ----- | ----- |\n",
    "| **0. Architecture of a classification neural network** | Neural networks can come in almost any shape or size, but they typically follow a similar floor plan. |\n",
    "| **1. Getting binary classification data ready** | Data can be almost anything but to get started we're going to create a simple binary classification dataset. |\n",
    "| **2. Building a PyTorch classification model** | Here we'll create a model to learn patterns in the data, we'll also choose a **loss function**, **optimizer** and build a **training loop** specific to classification. | \n",
    "| **3. Fitting the model to data (training)** | We've got data and a model, now let's let the model (try to) find patterns in the (**training**) data. |\n",
    "| **4. Making predictions and evaluating a model (inference)** | Our model's found patterns in the data, let's compare its findings to the actual (**testing**) data. |\n",
    "| **5. Improving a model (from a model perspective)** | We've trained and evaluated a model but it's not working, let's try a few things to improve it. |\n",
    "| **6. Non-linearity** | So far our model has only had the ability to model straight lines, what about non-linear (non-straight) lines? |\n",
    "| **7. Replicating non-linear functions** | We used **non-linear functions** to help model non-linear data, but what do these look like? |\n",
    "| **8. Putting it all together with multi-class classification** | Let's put everything we've done so far for binary classification together with a multi-class classification problem. |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uxdUc9OfHtgU"
   },
   "source": [
    "## Where can you get help?\n",
    "\n",
    "All of the materials for this course [live on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "And if you run into trouble, you can ask a question on the [Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions) there too.\n",
    "\n",
    "There's also the [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MSLHJiQxH4jU"
   },
   "source": [
    "## 0. Architecture of a classification neural network\n",
    "\n",
    "Before we get into writing code, let's look at the general architecture of a classification neural network.\n",
    "\n",
    "| **Hyperparameter** | **Binary Classification** | **Multiclass classification** |\n",
    "| --- | --- | --- |\n",
    "| **Input layer shape** (`in_features`) | Same as number of features (e.g. 5 for age, sex, height, weight, smoking status in heart disease prediction) | Same as binary classification |\n",
    "| **Hidden layer(s)** | Problem specific, minimum = 1, maximum = unlimited | Same as binary classification |\n",
    "| **Neurons per hidden layer** | Problem specific, generally 10 to 512 | Same as binary classification |\n",
    "| **Output layer shape** (`out_features`) | 1 (one class or the other) | 1 per class (e.g. 3 for food, person or dog photo) |\n",
    "| **Hidden layer activation** | Usually [ReLU](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html#torch.nn.ReLU) (rectified linear unit) but [can be many others](https://en.wikipedia.org/wiki/Activation_function#Table_of_activation_functions) | Same as binary classification |\n",
    "| **Output activation** | [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) ([`torch.sigmoid`](https://pytorch.org/docs/stable/generated/torch.sigmoid.html) in PyTorch)| [Softmax](https://en.wikipedia.org/wiki/Softmax_function) ([`torch.softmax`](https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html) in PyTorch) |\n",
    "| **Loss function** | [Binary crossentropy](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_loss_function_and_logistic_regression) ([`torch.nn.BCELoss`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) in PyTorch) | Cross entropy ([`torch.nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) in PyTorch) |\n",
    "| **Optimizer** | [SGD](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html) (stochastic gradient descent), [Adam](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html) (see [`torch.optim`](https://pytorch.org/docs/stable/optim.html) for more options) | Same as binary classification |\n",
    "\n",
    "Of course, this ingredient list of classification neural network components will vary depending on the problem you're working on.\n",
    "\n",
    "But it's more than enough to get started.\n",
    "\n",
    "We're going to get hands-on with this setup throughout this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VwvxFEjKHC71"
   },
   "source": [
    "## 1. Make classification data and get it ready\n",
    "\n",
    "Let's begin by making some data.\n",
    "\n",
    "We'll use the [`make_circles()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html) method from Scikit-Learn to generate two circles with different coloured dots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "RGeZvHsyHC72"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "\n",
    "\n",
    "# Make 1000 samples \n",
    "n_samples = 1000\n",
    "\n",
    "# Create circles\n",
    "X, y = make_circles(n_samples,\n",
    "                    noise=0.03, # a little bit of noise to the dots\n",
    "                    random_state=42) # keep random state so we get the same values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1FwwzJnQV2jv"
   },
   "source": [
    "Alright, now let's view the first 5 `X` and `y` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oAb8vcIhWEO8",
    "outputId": "b7316d88-7733-4981-9b4a-0a98c7cdd829"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 X features:\n",
      "[[ 0.75424625  0.23148074]\n",
      " [-0.75615888  0.15325888]\n",
      " [-0.81539193  0.17328203]\n",
      " [-0.39373073  0.69288277]\n",
      " [ 0.44220765 -0.89672343]]\n",
      "\n",
      "First 5 y labels:\n",
      "[1 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(f\"First 5 X features:\\n{X[:5]}\")\n",
    "print(f\"\\nFirst 5 y labels:\\n{y[:5]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ATakj2bVWBou"
   },
   "source": [
    "Looks like there's two `X` values per one `y` value. \n",
    "\n",
    "Let's keep following the data explorer's motto of *visualize, visualize, visualize* and put them into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "XAAqx_8sHC73",
    "outputId": "cd6ef4fe-cda3-48db-f2a5-9820660eab14"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.754246</td>\n",
       "      <td>0.231481</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.756159</td>\n",
       "      <td>0.153259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.815392</td>\n",
       "      <td>0.173282</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.393731</td>\n",
       "      <td>0.692883</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.442208</td>\n",
       "      <td>-0.896723</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.479646</td>\n",
       "      <td>0.676435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.013648</td>\n",
       "      <td>0.803349</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.771513</td>\n",
       "      <td>0.147760</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.169322</td>\n",
       "      <td>-0.793456</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.121486</td>\n",
       "      <td>1.021509</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2  label\n",
       "0  0.754246  0.231481      1\n",
       "1 -0.756159  0.153259      1\n",
       "2 -0.815392  0.173282      1\n",
       "3 -0.393731  0.692883      1\n",
       "4  0.442208 -0.896723      0\n",
       "5 -0.479646  0.676435      1\n",
       "6 -0.013648  0.803349      1\n",
       "7  0.771513  0.147760      1\n",
       "8 -0.169322 -0.793456      1\n",
       "9 -0.121486  1.021509      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make DataFrame of circle data\n",
    "import pandas as pd\n",
    "circles = pd.DataFrame({\"X1\": X[:, 0],\n",
    "    \"X2\": X[:, 1],\n",
    "    \"label\": y\n",
    "})\n",
    "circles.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FK2T7GpYW2BE"
   },
   "source": [
    "It looks like each pair of `X` features (`X1` and `X2`) has a label (`y`) value of either 0 or 1.\n",
    "\n",
    "This tells us that our problem is **binary classification** since there's only two options (0 or 1).\n",
    "\n",
    "How many values of each class are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cV8sVR9tHC74",
    "outputId": "dee5739f-1695-4e71-bb0b-de270f08b621"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    500\n",
       "0    500\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check different labels\n",
    "circles.label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ytQ5rm9eXa65"
   },
   "source": [
    "500 each, nice and balanced.\n",
    "\n",
    "Let's plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "ANkSmESCHC75",
    "outputId": "89b2f9ac-728c-481d-f4ba-6192a8334758"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize with a plot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x=X[:, 0], \n",
    "            y=X[:, 1], \n",
    "            c=y, \n",
    "            cmap=plt.cm.RdYlBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h0e1oR27HC76"
   },
   "source": [
    "Alrighty, looks like we've got a problem to solve.\n",
    "\n",
    "Let's find out how we could build a PyTorch neural network to classify dots into red (0) or blue (1).\n",
    "\n",
    "> **Note:** This dataset is often what's considered a **toy problem** (a problem that's used to try and test things out on) in machine learning. \n",
    "> \n",
    "> But it represents the major key of classification, you have some kind of data represented as numerical values and you'd like to build a model that's able to classify it, in our case, separate it into red or blue dots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ny6_J7F4HC76"
   },
   "source": [
    "### 1.1 Input and output shapes\n",
    "\n",
    "One of the most common errors in deep learning is shape errors.\n",
    "\n",
    "Mismatching the shapes of tensors and tensor operations will result in errors in your models.\n",
    "\n",
    "We're going to see plenty of these throughout the course.\n",
    "\n",
    "And there's no surefire way to make sure they won't happen, they will.\n",
    "\n",
    "What you can do instead is continually familiarize yourself with the shape of the data you're working with.\n",
    "\n",
    "I like referring to it as input and output shapes.\n",
    "\n",
    "Ask yourself:\n",
    "\n",
    "\"What shapes are my inputs and what shapes are my outputs?\"\n",
    "\n",
    "Let's find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r0h--vKdHC77",
    "outputId": "16e65bb5-83e1-49c7-af5e-90ecede4eeae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000, 2), (1000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shapes of our features and labels\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PW7zolShbfDg"
   },
   "source": [
    "Looks like we've got a match on the first dimension of each.\n",
    "\n",
    "There's 1000 `X` and 1000 `y`. \n",
    "\n",
    "But what's the second dimension on `X`?\n",
    "\n",
    "It often helps to view the values and shapes of a single sample (features and labels).\n",
    "\n",
    "Doing so will help you understand what input and output shapes you'd be expecting from your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k-L5zobVHC78",
    "outputId": "b3a96ca8-45f1-47d1-a98b-c2a7be79c0d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for one sample of X: [0.75424625 0.23148074] and the same for y: 1\n",
      "Shapes for one sample of X: (2,) and the same for y: ()\n"
     ]
    }
   ],
   "source": [
    "# View the first example of features and labels\n",
    "X_sample = X[0]\n",
    "y_sample = y[0]\n",
    "print(f\"Values for one sample of X: {X_sample} and the same for y: {y_sample}\")\n",
    "print(f\"Shapes for one sample of X: {X_sample.shape} and the same for y: {y_sample.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vn692XaRaifl"
   },
   "source": [
    "This tells us the second dimension for `X` means it has two features (vector) where as `y` has a single feature (scalar).\n",
    "\n",
    "We have two inputs for one output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eLyDPN6ZR_ho"
   },
   "source": [
    "### 1.2 Turn data into tensors and create train and test splits\n",
    "\n",
    "We've investigated the input and output shapes of our data, now let's prepare it for being used with PyTorch and for modelling.\n",
    "\n",
    "Specifically, we'll need to:\n",
    "1. Turn our data into tensors (right now our data is in NumPy arrays and PyTorch prefers to work with PyTorch tensors).\n",
    "2. Split our data into training and test sets (we'll train a model on the training set to learn the patterns between `X` and `y` and then evaluate those learned patterns on the test dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z2gcR31aHC78",
    "outputId": "cd4e10c1-c358-4b74-81e0-bab7610197cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.7542,  0.2315],\n",
       "         [-0.7562,  0.1533],\n",
       "         [-0.8154,  0.1733],\n",
       "         [-0.3937,  0.6929],\n",
       "         [ 0.4422, -0.8967]]),\n",
       " tensor([1., 1., 1., 1., 0.]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn data into tensors\n",
    "# Otherwise this causes issues with computations later on\n",
    "import torch\n",
    "X = torch.from_numpy(X).type(torch.float)\n",
    "y = torch.from_numpy(y).type(torch.float)\n",
    "\n",
    "# View the first five samples\n",
    "X[:5], y[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r9XNJv8lfmRG"
   },
   "source": [
    "Now our data is in tensor format, let's split it into training and test sets.\n",
    "\n",
    "To do so, let's use the helpful function [`train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) from Scikit-Learn.\n",
    "\n",
    "We'll use `test_size=0.2` (80% training, 20% testing) and because the split happens randomly across the data, let's use `random_state=42` so the split is reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DW6PU82BHC79",
    "outputId": "a2d3f3df-701e-44ef-a506-fd31d5443e90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 200, 800, 200)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into train and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2, # 20% test, 80% train\n",
    "                                                    random_state=42) # make the random split reproducible\n",
    "\n",
    "len(X_train), len(X_test), len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t-wYDWy9gSRc"
   },
   "source": [
    "Nice! Looks like we've now got 800 training samples and 200 testing samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iCyQ93VTHC79"
   },
   "source": [
    "## 2. Building a model\n",
    "\n",
    "We've got some data ready, now it's time to build a model.\n",
    "\n",
    "We'll break it down into a few parts.\n",
    "\n",
    "1. Setting up device agnostic code (so our model can run on CPU or GPU if it's available).\n",
    "2. Constructing a model by subclassing `nn.Module`.\n",
    "3. Defining a loss function and optimizer.\n",
    "4. Creating a training loop (this'll be in the next section).\n",
    "\n",
    "The good news is we've been through all of the above steps before in notebook 01.\n",
    "\n",
    "Except now we'll be adjusting them so they work with a classification dataset.\n",
    "\n",
    "Let's start by importing PyTorch and `torch.nn` as well as setting up device agnostic code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "2itMTezPRnVR",
    "outputId": "507bf4a3-b5ae-4943-aa21-4eb181b0d741"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard PyTorch imports\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "# Make device agnostic code\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1Fil1v04nXr"
   },
   "source": [
    "Excellent, now `device` is setup, we can use it for any data or models we create and PyTorch will handle it on the CPU (default) or GPU if it's available.\n",
    "\n",
    "How about we create a model?\n",
    "\n",
    "We'll want a model capable of handling our `X` data as inputs and producing something in the shape of our `y` data as outputs.\n",
    "\n",
    "In other words, given `X` (features) we want our model to predict `y` (label).\n",
    "\n",
    "This setup where you have features and labels is referred to as **supervised learning**. Because your data is telling your model what the outputs should be given a certain input.\n",
    "\n",
    "To create such a model it'll need to handle the input and output shapes of `X` and `y`.\n",
    "\n",
    "Remember how I said input and output shapes are important? Here we'll see why.\n",
    "\n",
    "Let's create a model class that:\n",
    "1. Subclasses `nn.Module` (almost all PyTorch models are subclasses of `nn.Module`).\n",
    "2. Creates 2 `nn.Linear` layers in the constructor capable of handling the input and output shapes of `X` and `y`.\n",
    "3. Defines a `forward()` method containing the forward pass computation of the model.\n",
    "4. Instantiates the model class and sends it to the target `device`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C_EsAE3VHC7-",
    "outputId": "d9c976ea-e23f-4993-c847-a15b0bf7b0b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CircleModelV0(\n",
       "  (layer_1): Linear(in_features=2, out_features=5, bias=True)\n",
       "  (layer_2): Linear(in_features=5, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Construct a model class that subclasses nn.Module\n",
    "class CircleModelV0(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 2. Create 2 nn.Linear layers capable of handling X and y input and output shapes\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=5) # takes in 2 features (X), produces 5 features\n",
    "        self.layer_2 = nn.Linear(in_features=5, out_features=1) # takes in 5 features, produces 1 feature (y)\n",
    "    \n",
    "    # 3. Define a forward method containing the forward pass computation\n",
    "    def forward(self, x):\n",
    "        # Return the output of layer_2, a single feature, the same shape as y\n",
    "        return self.layer_2(self.layer_1(x)) # computation goes through layer_1 first then the output of layer_1 goes through layer_2\n",
    "\n",
    "# 4. Create an instance of the model and send it to target device\n",
    "model_0 = CircleModelV0().to(device)\n",
    "model_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TtdkMniZ7KyK"
   },
   "source": [
    "What's going on here?\n",
    "\n",
    "We've seen a few of these steps before.\n",
    "\n",
    "The only major change is what's happening between `self.layer_1` and `self.layer_2`.\n",
    "\n",
    "`self.layer_1` takes 2 input features `in_features=2` and produces 5 output features `out_features=5`.\n",
    "\n",
    "This is known as having 5 **hidden units** or **neurons**.\n",
    "\n",
    "This layer turns the input data from having 2 features to 5 features.\n",
    "\n",
    "Why do this?\n",
    "\n",
    "This allows the model to learn patterns from 5 numbers rather than just 2 numbers, *potentially* leading to better outputs.\n",
    "\n",
    "I say potentially because sometimes it doesn't work.\n",
    "\n",
    "The number of hidden units you can use in neural network layers is a **hyperparameter** (a value you can set yourself) and there's no set in stone value you have to use.\n",
    "\n",
    "Generally more is better but there's also such a thing as too much. The amount you choose will depend on your model type and dataset you're working with. \n",
    "\n",
    "Since our dataset is small and simple, we'll keep it small.\n",
    "\n",
    "The only rule with hidden units is that the next layer, in our case, `self.layer_2` has to take the same `in_features` as the previous layer `out_features`.\n",
    "\n",
    "That's why `self.layer_2` has `in_features=5`, it takes the `out_features=5` from `self.layer_1` and performs a linear computation on them, turning them into `out_features=1` (the same shape as `y`).\n",
    "\n",
    "![A visual example of what a classification neural network with linear activation looks like on the tensorflow playground](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-tensorflow-playground-linear-activation.png)\n",
    "*A visual example of what a similar classification neural network to the one we've just built looks like. Try creating one of your own on the [TensorFlow Playground website](https://playground.tensorflow.org/).*\n",
    "\n",
    "You can also do the same as above using [`nn.Sequential`](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html).\n",
    "\n",
    "`nn.Sequential` performs a forward pass computation of the input data through the layers in the order they appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H7I8GyLjHC7_",
    "outputId": "def65504-863a-4361-816e-909dbfa4d624"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=2, out_features=5, bias=True)\n",
       "  (1): Linear(in_features=5, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replicate CircleModelV0 with nn.Sequential\n",
    "model_0 = nn.Sequential(\n",
    "    nn.Linear(in_features=2, out_features=5),\n",
    "    nn.Linear(in_features=5, out_features=1)\n",
    ").to(device)\n",
    "\n",
    "model_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MiRHq9mxFIf-"
   },
   "source": [
    "Woah, that looks much simpler than subclassing `nn.Module`, why not just always use `nn.Sequential`?\n",
    "\n",
    "`nn.Sequential` is fantastic for straight-forward computations, however, as the namespace says, it *always* runs in sequential order.\n",
    "\n",
    "So if you'd like something else to happen (rather than just straight-forward sequential computation) you'll want to define your own custom `nn.Module` subclass.\n",
    "\n",
    "Now we've got a model, let's see what happens when we pass some data through it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tt339-_CC8sz",
    "outputId": "a4014167-4181-434e-ab75-905094229b3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of predictions: 200, Shape: torch.Size([200, 1])\n",
      "Length of test samples: 200, Shape: torch.Size([200])\n",
      "\n",
      "First 10 predictions:\n",
      "tensor([[-0.4279],\n",
      "        [-0.3417],\n",
      "        [-0.5975],\n",
      "        [-0.3801],\n",
      "        [-0.5078],\n",
      "        [-0.4559],\n",
      "        [-0.2842],\n",
      "        [-0.3107],\n",
      "        [-0.6010],\n",
      "        [-0.3350]], device='cuda:0', grad_fn=<SliceBackward0>)\n",
      "\n",
      "First 10 test labels:\n",
      "tensor([1., 0., 1., 0., 1., 1., 0., 0., 1., 0.])\n"
     ]
    }
   ],
   "source": [
    "# Make predictions with the model\n",
    "untrained_preds = model_0(X_test.to(device))\n",
    "print(f\"Length of predictions: {len(untrained_preds)}, Shape: {untrained_preds.shape}\")\n",
    "print(f\"Length of test samples: {len(y_test)}, Shape: {y_test.shape}\")\n",
    "print(f\"\\nFirst 10 predictions:\\n{untrained_preds[:10]}\")\n",
    "print(f\"\\nFirst 10 test labels:\\n{y_test[:10]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q7v8TVnqGMZh"
   },
   "source": [
    "Hmm, it seems there are the same amount of predictions as there are test labels but the predictions don't look like they're in the same form or shape as the test labels.\n",
    "\n",
    "We've got a couple steps we can do to fix this, we'll see these later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8aoQn39pHC7_"
   },
   "source": [
    "### 2.1 Setup loss function and optimizer\n",
    "\n",
    "We've setup a loss (also called a criterion or cost function) and optimizer before in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/#creating-a-loss-function-and-optimizer-in-pytorch).\n",
    "\n",
    "But different problem types require different loss functions. \n",
    "\n",
    "For example, for a regression problem (predicting a number) you might use mean absolute error (MAE) loss.\n",
    "\n",
    "And for a binary classification problem (like ours), you'll often use [binary cross entropy](https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a) as the loss function.\n",
    "\n",
    "However, the same optimizer function can often be used across different problem spaces.\n",
    "\n",
    "For example, the stochastic gradient descent optimizer (SGD, `torch.optim.SGD()`) can be used for a range of problems, and the same applies to the Adam optimizer (`torch.optim.Adam()`). \n",
    "\n",
    "| Loss function/Optimizer | Problem type | PyTorch Code |\n",
    "| ----- | ----- | ----- |\n",
    "| Stochastic Gradient Descent (SGD) optimizer | Classification, regression, many others. | [`torch.optim.SGD()`](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html) |\n",
    "| Adam Optimizer | Classification, regression, many others. | [`torch.optim.Adam()`](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html) |\n",
    "| Binary cross entropy loss | Binary classification | [`torch.nn.BCELossWithLogits`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) or [`torch.nn.BCELoss`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) |\n",
    "| Cross entropy loss | Multi-class classification | [`torch.nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) |\n",
    "| Mean absolute error (MAE) or L1 Loss | Regression | [`torch.nn.L1Loss`](https://pytorch.org/docs/stable/generated/torch.nn.L1Loss.html) | \n",
    "| Mean squared error (MSE) or L2 Loss | Regression | [`torch.nn.MSELoss`](https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html#torch.nn.MSELoss) |  \n",
    "\n",
    "*Table of various loss functions and optimizers, there are more but these are some common ones you'll see.*\n",
    "\n",
    "Since we're working with a binary classification problem, let's use a binary cross entropy loss function.\n",
    "\n",
    "> **Note:** Recall a **loss function** is what measures how *wrong* your model predictions are, the higher the loss, the worse your model.\n",
    ">\n",
    "> Also, PyTorch documentation often refers to loss functions as \"loss criterion\" or \"criterion\", these are all different ways of describing the same thing.\n",
    "\n",
    "PyTorch has two binary cross entropy implementations:\n",
    "1. [`torch.nn.BCELoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) - Creates a loss function that measures the binary cross entropy between the target (label) and input (features).\n",
    "2. [`torch.nn.BCEWithLogitsLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) - This is the same as above except it has a sigmoid layer ([`nn.Sigmoid`](https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html)) built-in (we'll see what this means soon).\n",
    "\n",
    "Which one should you use? \n",
    "\n",
    "The [documentation for `torch.nn.BCEWithLogitsLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) states that it's more numerically stable than using `torch.nn.BCELoss()` after a `nn.Sigmoid` layer. \n",
    "\n",
    "So generally, implementation 2 is a better option. However for advanced usage, you may want to separate the combination of `nn.Sigmoid` and `torch.nn.BCELoss()` but that is beyond the scope of this notebook.\n",
    "\n",
    "Knowing this, let's create a loss function and an optimizer. \n",
    "\n",
    "For the optimizer we'll use `torch.optim.SGD()` to optimize the model parameters with learning rate 0.1.\n",
    "\n",
    "> **Note:** There's a [discussion on the PyTorch forums about the use of `nn.BCELoss` vs. `nn.BCEWithLogitsLoss`](https://discuss.pytorch.org/t/bceloss-vs-bcewithlogitsloss/33586/4). It can be confusing at first but as with many things, it becomes easier with practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "DjpQsdOZHC7_"
   },
   "outputs": [],
   "source": [
    "# Create a loss function\n",
    "# loss_fn = nn.BCELoss() # BCELoss = no sigmoid built-in\n",
    "loss_fn = nn.BCEWithLogitsLoss() # BCEWithLogitsLoss = sigmoid built-in\n",
    "\n",
    "# Create an optimizer\n",
    "optimizer = torch.optim.SGD(params=model_0.parameters(), \n",
    "                            lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RKmi3fp9wYnV"
   },
   "source": [
    "Now let's also create an **evaluation metric**.\n",
    "\n",
    "An evaluation metric can be used to offer another perspective on how your model is going.\n",
    "\n",
    "If a loss function measures how *wrong* your model is, I like to think of evaluation metrics as measuring how *right* it is.\n",
    "\n",
    "Of course, you could argue both of these are doing the same thing but evaluation metrics offer a different perspective.\n",
    "\n",
    "After all, when evaluating your models it's good to look at things from multiple points of view.\n",
    "\n",
    "There are several evaluation metrics that can be used for classification problems but let's start out with **accuracy**.\n",
    "\n",
    "Accuracy can be measured by dividing the total number of correct predictions over the total number of predictions.\n",
    "\n",
    "For example, a model that makes 99 correct predictions out of 100 will have an accuracy of 99%.\n",
    "\n",
    "Let's write a function to do so.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "RcaZQR0mHC7_"
   },
   "outputs": [],
   "source": [
    "# Calculate accuracy (a classification metric)\n",
    "def accuracy_fn(y_true, y_pred):\n",
    "    correct = torch.eq(y_true, y_pred).sum().item() # torch.eq() calculates where two tensors are equal\n",
    "    acc = (correct / len(y_pred)) * 100 \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OuplloDPxviL"
   },
   "source": [
    "Excellent! We can now use this function whilst training our model to measure it's performance alongside the loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4UpJKZ8PHC8A"
   },
   "source": [
    "## 3. Train model\n",
    "\n",
    "Okay, now we've got a loss function and optimizer ready to go, let's train a model.\n",
    "\n",
    "Do you remember the steps in a PyTorch training loop?\n",
    "\n",
    "If not, here's a reminder.\n",
    "\n",
    "Steps in training:\n",
    "\n",
    "<details>\n",
    "    <summary>PyTorch training loop steps</summary>\n",
    "    <ol>\n",
    "        <li><b>Forward pass</b> - The model goes through all of the training data once, performing its\n",
    "            <code>forward()</code> function\n",
    "            calculations (<code>model(x_train)</code>).\n",
    "        </li>\n",
    "        <li><b>Calculate the loss</b> - The model's outputs (predictions) are compared to the ground truth and evaluated\n",
    "            to see how\n",
    "            wrong they are (<code>loss = loss_fn(y_pred, y_train</code>).</li>\n",
    "        <li><b>Zero gradients</b> - The optimizers gradients are set to zero (they are accumulated by default) so they\n",
    "            can be\n",
    "            recalculated for the specific training step (<code>optimizer.zero_grad()</code>).</li>\n",
    "        <li><b>Perform backpropagation on the loss</b> - Computes the gradient of the loss with respect for every model\n",
    "            parameter to\n",
    "            be updated (each parameter\n",
    "            with <code>requires_grad=True</code>). This is known as <b>backpropagation</b>, hence \"backwards\"\n",
    "            (<code>loss.backward()</code>).</li>\n",
    "        <li><b>Step the optimizer (gradient descent)</b> - Update the parameters with <code>requires_grad=True</code>\n",
    "            with respect to the loss\n",
    "            gradients in order to improve them (<code>optimizer.step()</code>).</li>\n",
    "    </ol>\n",
    "</details>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NMeDqPpsnst6"
   },
   "source": [
    "### 3.1 Going from raw model outputs to predicted labels (logits -> prediction probabilities -> prediction labels)\n",
    "\n",
    "Before the training loop steps, let's see what comes out of our model during the forward pass (the forward pass is defined by the `forward()` method).\n",
    "\n",
    "To do so, let's pass the model some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e5stsbDl0zKl",
    "outputId": "829a0842-0a37-455a-b058-3e547200f836"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.4279],\n",
       "        [-0.3417],\n",
       "        [-0.5975],\n",
       "        [-0.3801],\n",
       "        [-0.5078]], device='cuda:0', grad_fn=<SliceBackward0>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the frist 5 outputs of the forward pass on the test data\n",
    "y_logits = model_0(X_test.to(device))[:5]\n",
    "y_logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b1sNl8D8fWXm"
   },
   "source": [
    "Since our model hasn't been trained, these outputs are basically random.\n",
    "\n",
    "But *what* are they?\n",
    "\n",
    "They're the output of our `forward()` method.\n",
    "\n",
    "Which implements two layers of `nn.Linear()` which internally calls the following equation:\n",
    "\n",
    "$$\n",
    "\\mathbf{y} = x \\cdot \\mathbf{Weights}^T  + \\mathbf{bias}\n",
    "$$\n",
    "\n",
    "The *raw outputs* (unmodified) of this equation ($\\mathbf{y}$) and in turn, the raw outputs of our model are often referred to as [**logits**](https://datascience.stackexchange.com/a/31045).\n",
    "\n",
    "That's what our model is outputing above when it takes in the input data ($x$ in the equation or `X_test` in the code), logits.\n",
    "\n",
    "However, these numbers are hard to interpret.\n",
    "\n",
    "We'd like some numbers that are comparable to our truth labels.\n",
    "\n",
    "To get our model's raw outputs (logits) into such a form, we can use the [sigmoid activation function](https://pytorch.org/docs/stable/generated/torch.sigmoid.html).\n",
    "\n",
    "Let's try it out.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QGC7UmBi0s6E",
    "outputId": "3564ee3b-e34f-40a3-a40a-b9c9b948da04"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3946],\n",
       "        [0.4154],\n",
       "        [0.3549],\n",
       "        [0.4061],\n",
       "        [0.3757]], device='cuda:0', grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use sigmoid on model logits\n",
    "y_pred_probs = torch.sigmoid(y_logits)\n",
    "y_pred_probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HxmWz_GVjZoB"
   },
   "source": [
    "Okay, it seems like the outputs now have some kind of consistency (even though they're still random).\n",
    "\n",
    "They're now in the form of **prediction probabilities** (I usually refer to these as `y_pred_probs`), in other words, the values are now how much the model thinks the data point belongs to one class or another.\n",
    "\n",
    "In our case, since we're dealing with binary classification, our ideal outputs are 0 or 1.\n",
    "\n",
    "So these values can be viewed as a decision boundary.\n",
    "\n",
    "The closer to 0, the more the model thinks the sample belongs to class 0, the closer to 1, the more the model thinks the sample belongs to class 1.\n",
    "\n",
    "More specificially:\n",
    "* If `y_pred_probs` >= 0.5, `y=1` (class 1)\n",
    "* If `y_pred_probs` < 0.5, `y=0` (class 0)\n",
    "\n",
    "To turn our prediction probabilities into prediction labels, we can round the outputs of the sigmoid activation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "naZxlTTwlMwX",
    "outputId": "6134e47d-bbb2-46c3-e2ec-b4f42d517e39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([True, True, True, True, True], device='cuda:0')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0.], device='cuda:0', grad_fn=<SqueezeBackward0>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the predicted labels (round the prediction probabilities)\n",
    "y_preds = torch.round(y_pred_probs)\n",
    "\n",
    "# In full\n",
    "y_pred_labels = torch.round(torch.sigmoid(model_0(X_test.to(device))[:5]))\n",
    "\n",
    "# Check for equality\n",
    "print(torch.eq(y_preds.squeeze(), y_pred_labels.squeeze()))\n",
    "\n",
    "# Get rid of extra dimension\n",
    "y_preds.squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5cMsgFWWmPLU"
   },
   "source": [
    "Excellent! Now it looks like our model's predictions are in the same form as our truth labels (`y_test`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MaQ0CN4ZmU1W",
    "outputId": "6b1cd7b4-f1d0-49b5-a8c3-338cf75c6cb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 1., 0., 1.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NXqUulG3maPH"
   },
   "source": [
    "This means we'll be able to compare our model's predictions to the test labels to see how well it's performing. \n",
    "\n",
    "To recap, we converted our model's raw outputs (logits) to prediction probabilities using a sigmoid activation function.\n",
    "\n",
    "And then converted the prediction probabilities to prediction labels by rounding them.\n",
    "\n",
    "> **Note:** The use of the sigmoid activation function is often only for binary classification logits. For multi-class classification, we'll be looking at using the [softmax activation function](https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html) (this will come later on).\n",
    ">\n",
    "> And the use of the sigmoid activation function is not required when passing our model's raw outputs to the `nn.BCEWithLogitsLoss` (the \"logits\" in logits loss is because it works on the model's raw logits output), this is because it has a sigmoid function built-in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Va7gg8yxn6Sg"
   },
   "source": [
    "### 3.2 Building a training and testing loop\n",
    "\n",
    "Alright, we've discussed how to take our raw model outputs and convert them to prediction labels, now let's build a training loop.\n",
    "\n",
    "Let's start by training for 100 epochs and outputing the model's progress every 10 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DFABVGo2HC8A",
    "outputId": "e0341074-b603-41d5-a389-c401b4934d73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.72090, Accuracy: 50.00% | Test loss: 0.72196, Test acc: 50.00%\n",
      "Epoch: 10 | Loss: 0.70291, Accuracy: 50.00% | Test loss: 0.70542, Test acc: 50.00%\n",
      "Epoch: 20 | Loss: 0.69659, Accuracy: 50.00% | Test loss: 0.69942, Test acc: 50.00%\n",
      "Epoch: 30 | Loss: 0.69432, Accuracy: 43.25% | Test loss: 0.69714, Test acc: 41.00%\n",
      "Epoch: 40 | Loss: 0.69349, Accuracy: 47.00% | Test loss: 0.69623, Test acc: 46.50%\n",
      "Epoch: 50 | Loss: 0.69319, Accuracy: 49.00% | Test loss: 0.69583, Test acc: 46.00%\n",
      "Epoch: 60 | Loss: 0.69308, Accuracy: 50.12% | Test loss: 0.69563, Test acc: 46.50%\n",
      "Epoch: 70 | Loss: 0.69303, Accuracy: 50.38% | Test loss: 0.69551, Test acc: 46.00%\n",
      "Epoch: 80 | Loss: 0.69302, Accuracy: 51.00% | Test loss: 0.69543, Test acc: 46.00%\n",
      "Epoch: 90 | Loss: 0.69301, Accuracy: 51.00% | Test loss: 0.69537, Test acc: 46.00%\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epochs\n",
    "epochs = 100\n",
    "\n",
    "# Put data to target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "# Build training and evaluation loop\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    model_0.train()\n",
    "\n",
    "    # 1. Forward pass (model outputs raw logits)\n",
    "    y_logits = model_0(X_train).squeeze() # squeeze to remove extra `1` dimensions, this won't work unless model and data are on same device \n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # turn logits -> pred probs -> pred labls\n",
    "  \n",
    "    # 2. Calculate loss/accuracy\n",
    "    # loss = loss_fn(torch.sigmoid(y_logits), # Using nn.BCELoss you need torch.sigmoid()\n",
    "    #                y_train) \n",
    "    loss = loss_fn(y_logits, # Using nn.BCEWithLogitsLoss works with raw logits\n",
    "                   y_train) \n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred) \n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_0.eval()\n",
    "    with torch.inference_mode():\n",
    "        # 1. Forward pass\n",
    "        test_logits = model_0(X_test).squeeze() \n",
    "        test_pred = torch.round(torch.sigmoid(test_logits))\n",
    "        # 2. Caculate loss/accuracy\n",
    "        test_loss = loss_fn(test_logits,\n",
    "                            y_test)\n",
    "        test_acc = accuracy_fn(y_true=y_test,\n",
    "                               y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening every 10 epochs\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "It1Xy_f5perA"
   },
   "source": [
    "Hmm, what do you notice about the performance of our model?\n",
    "\n",
    "It looks like it went through the training and testing steps fine but the results don't seem to have moved too much.\n",
    "\n",
    "The accuracy barely moves above 50% on each data split.\n",
    "\n",
    "And because we're working with a balanced binary classification problem, it means our model is performing as good as random guessing (with 500 samples of class 0 and class 1 a model predicting class 1 every single time would achieve 50% accuracy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WCeyddo-HC8A"
   },
   "source": [
    "## 4. Make predictions and evaluate the model\n",
    "\n",
    "From the metrics it looks like our model is random guessing.\n",
    "\n",
    "How could we investigate this further?\n",
    "\n",
    "I've got an idea.\n",
    "\n",
    "The data explorer's motto!\n",
    "\n",
    "\"Visualize, visualize, visualize!\"\n",
    "\n",
    "Let's make a plot of our model's predictions, the data it's trying to predict on and the decision boundary it's creating for whether something is class 0 or class 1.\n",
    "\n",
    "To do so, we'll write some code to download and import the [`helper_functions.py` script](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) from the [Learn PyTorch for Deep Learning repo](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "It contains a helpful function called `plot_decision_boundary()` which creates a NumPy meshgrid to visually plot the different points where our model is predicting certain classes.\n",
    "\n",
    "We'll also import `plot_predictions()` which we wrote in notebook 01 to use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QakJVGI8t2gB",
    "outputId": "4f63a8a3-5eae-49fb-e17e-df6c5721ab5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "helper_functions.py already exists, skipping download\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daniel/.local/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /home/daniel/.local/lib/python3.8/site-packages/torchvision/image.so: undefined symbol: _ZN3c106detail19maybe_wrap_dim_slowIlEET_S2_S2_b\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from pathlib import Path \n",
    "\n",
    "# Download helper functions from Learn PyTorch repo (if not already downloaded)\n",
    "if Path(\"helper_functions.py\").is_file():\n",
    "  print(\"helper_functions.py already exists, skipping download\")\n",
    "else:\n",
    "  print(\"Downloading helper_functions.py\")\n",
    "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
    "  with open(\"helper_functions.py\", \"wb\") as f:\n",
    "    f.write(request.content)\n",
    "\n",
    "from helper_functions import plot_predictions, plot_decision_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "bEbDUTKjHC8B",
    "outputId": "aa344fe8-0207-43df-f0e9-61b9302459a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_0, X_train, y_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_0, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aeHx9MbTvrJH"
   },
   "source": [
    "Oh wow, it seems like we've found the cause of model's performance issue.\n",
    "\n",
    "It's currently trying to split the red and blue dots using a straight line...\n",
    "\n",
    "That explains the 50% accuracy. Since our data is circular, drawing a straight line can at best cut it down the middle.\n",
    "\n",
    "In machine learning terms, our model is **underfitting**, meaning it's not learning predictive patterns from the data.\n",
    "\n",
    "How could we improve this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6VivsLTmHC8B"
   },
   "source": [
    "## 5. Improving a model (from a model perspective) \n",
    "\n",
    "Let's try to fix our model's underfitting problem.\n",
    "\n",
    "Focusing specifically on the model (not the data), there are a few ways we could do this.\n",
    "\n",
    "| Model improvement technique* | What does it do? |\n",
    "| ----- | ----- |\n",
    "| **Add more layers** | Each layer *potentially* increases the learning capabilities of the model with each layer being able to learn some kind of new pattern in the data. More layers are often referred to as making your neural network *deeper*. |\n",
    "| **Add more hidden units** | Similar to the above, more hidden units per layer means a *potential* increase in learning capabilities of the model. More hidden units are often referred to as making your neural network *wider*. |\n",
    "| **Fitting for longer (more epochs)** | Your model might learn more if it had more opportunities to look at the data. |\n",
    "| **Changing the activation functions** | Some data just can't be fit with only straight lines (like what we've seen), using non-linear activation functions can help with this (hint, hint). |\n",
    "| **Change the learning rate** | Less model specific, but still related, the learning rate of the optimizer decides how much a model should change its parameters each step, too much and the model overcorrects, too little and it doesn't learn enough. |\n",
    "| **Change the loss function** | Again, less model specific but still important, different problems require different loss functions. For example, a binary cross entropy loss function won't work with a multi-class classification problem. |\n",
    "| **Use transfer learning** | Take a pretrained model from a problem domain similar to yours and adjust it to your own problem. We cover transfer learning in [notebook 06](https://www.learnpytorch.io/06_pytorch_transfer_learning/). |\n",
    "\n",
    "> **Note:** *because you can adjust all of these by hand, they're referred to as **hyperparameters**. \n",
    ">\n",
    "> And this is also where machine learning's half art half science comes in, there's no real way to know here what the best combination of values is for your project, best to follow the data scientist's motto of \"experiment, experiment, experiment\".\n",
    "\n",
    "Let's see what happens if we add an extra layer to our model, fit for longer (`epochs=1000` instead of `epochs=100`) and increase the number of hidden units from `5` to `10`.\n",
    "\n",
    "We'll follow the same steps we did above but with a few changed hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j-GRUN9QHC8B",
    "outputId": "9174e74b-862a-4bca-9f17-c7176d22f81e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CircleModelV1(\n",
       "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
       "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class CircleModelV1(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=10)\n",
    "        self.layer_2 = nn.Linear(in_features=10, out_features=10) # extra layer\n",
    "        self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
    "        \n",
    "    def forward(self, x): # note: always make sure forward is spelt correctly!\n",
    "        # Creating a model like this is the same as below, though below\n",
    "        # generally benefits from speedups where possible.\n",
    "        # z = self.layer_1(x)\n",
    "        # z = self.layer_2(z)\n",
    "        # z = self.layer_3(z)\n",
    "        # return z\n",
    "        return self.layer_3(self.layer_2(self.layer_1(x)))\n",
    "\n",
    "model_1 = CircleModelV1().to(device)\n",
    "model_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ACkcim2k2G5R"
   },
   "source": [
    "Now we've got a model, we'll recreate a loss function and optimizer instance, using the same settings as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "AXYwYPpSHC8B"
   },
   "outputs": [],
   "source": [
    "# loss_fn = nn.BCELoss() # Requires sigmoid on input\n",
    "loss_fn = nn.BCEWithLogitsLoss() # Does not require sigmoid on input\n",
    "optimizer = torch.optim.SGD(model_1.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "drHt2W7x1JEW"
   },
   "source": [
    "Beautiful, model, optimizer and loss function ready, let's make a training loop.\n",
    "\n",
    "This time we'll train for longer (`epochs=1000` vs `epochs=100`) and see if it improves our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aX0QGBozHC8C",
    "outputId": "b00e48e9-1075-4f6e-c0c3-e511451d3fe9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.69396, Accuracy: 50.88% | Test loss: 0.69261, Test acc: 51.00%\n",
      "Epoch: 100 | Loss: 0.69305, Accuracy: 50.38% | Test loss: 0.69379, Test acc: 48.00%\n",
      "Epoch: 200 | Loss: 0.69299, Accuracy: 51.12% | Test loss: 0.69437, Test acc: 46.00%\n",
      "Epoch: 300 | Loss: 0.69298, Accuracy: 51.62% | Test loss: 0.69458, Test acc: 45.00%\n",
      "Epoch: 400 | Loss: 0.69298, Accuracy: 51.12% | Test loss: 0.69465, Test acc: 46.00%\n",
      "Epoch: 500 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69467, Test acc: 46.00%\n",
      "Epoch: 600 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 700 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 800 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 900 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "epochs = 1000 # Train for longer\n",
    "\n",
    "# Put data to target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_1(X_train).squeeze()\n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "\n",
    "    # 2. Calculate loss/accuracy\n",
    "    loss = loss_fn(y_logits, y_train)\n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_1.eval()\n",
    "    with torch.inference_mode():\n",
    "        # 1. Forward pass\n",
    "        test_logits = model_1(X_test).squeeze() \n",
    "        test_pred = torch.round(torch.sigmoid(test_logits))\n",
    "        # 2. Caculate loss/accuracy\n",
    "        test_loss = loss_fn(test_logits,\n",
    "                            y_test)\n",
    "        test_acc = accuracy_fn(y_true=y_test,\n",
    "                               y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening every 10 epochs\n",
    "    if epoch % 100 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o0ca3sIV1WrZ"
   },
   "source": [
    "What? Our model trained for longer and with an extra layer but it still looks like it didn't learn any patterns better than random guessing.\n",
    "\n",
    "Let's visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "GUjJqRw7HC8C",
    "outputId": "12d163f9-b602-459e-f34b-54c58abf8d7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_1, X_train, y_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_1, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8UqtuZYwHC8C"
   },
   "source": [
    "Hmmm.\n",
    "\n",
    "Our model is still drawing a straight line between the red and blue dots.\n",
    "\n",
    "If our model is drawing a straight line, could it model linear data? Like we did in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nam5esXj2Mj_"
   },
   "source": [
    "### 5.1 Preparing data to see if our model can model a straight line\n",
    "Let's create some linear data to see if our model's able to model it and we're not just using a model that can't learn anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xLoEBQ6fHC8C",
    "outputId": "176a4674-3142-420c-bace-97cdbfbf473e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([[0.0000],\n",
       "         [0.0100],\n",
       "         [0.0200],\n",
       "         [0.0300],\n",
       "         [0.0400]]),\n",
       " tensor([[0.3000],\n",
       "         [0.3070],\n",
       "         [0.3140],\n",
       "         [0.3210],\n",
       "         [0.3280]]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create some data (same as notebook 01)\n",
    "weight = 0.7\n",
    "bias = 0.3\n",
    "start = 0\n",
    "end = 1\n",
    "step = 0.01\n",
    "\n",
    "# Create data\n",
    "X_regression = torch.arange(start, end, step).unsqueeze(dim=1)\n",
    "y_regression = weight * X_regression + bias # linear regression formula\n",
    "\n",
    "# Check the data\n",
    "print(len(X_regression))\n",
    "X_regression[:5], y_regression[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wquTX_wX275-"
   },
   "source": [
    "Wonderful, now let's split our data into training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UIoO2k8yHC8D",
    "outputId": "2d9144d9-18d9-427a-a898-7a5c920cd9aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 80 20 20\n"
     ]
    }
   ],
   "source": [
    "# Create train and test splits\n",
    "train_split = int(0.8 * len(X_regression)) # 80% of data used for training set\n",
    "X_train_regression, y_train_regression = X_regression[:train_split], y_regression[:train_split]\n",
    "X_test_regression, y_test_regression = X_regression[train_split:], y_regression[train_split:]\n",
    "\n",
    "# Check the lengths of each split\n",
    "print(len(X_train_regression), \n",
    "    len(y_train_regression), \n",
    "    len(X_test_regression), \n",
    "    len(y_test_regression))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sQtonoMn3s90"
   },
   "source": [
    "Beautiful, let's see how the data looks.\n",
    "\n",
    "To do so, we'll use the `plot_predictions()` function we created in notebook 01. \n",
    "\n",
    "It's contained within the [`helper_functions.py` script](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) on the Learn PyTorch for Deep Learning repo which we downloaded above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "pcg5OvncHC8D",
    "outputId": "77b50411-c589-4d5f-e7ca-d03b1b9a68cc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(train_data=X_train_regression,\n",
    "    train_labels=y_train_regression,\n",
    "    test_data=X_test_regression,\n",
    "    test_labels=y_test_regression\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZRflODP66kG"
   },
   "source": [
    "### 5.2 Adjusting `model_1` to fit a straight line\n",
    "\n",
    "Now we've got some data, let's recreate `model_1` but with a loss function suited to our regression data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rc13zblAHC8D",
    "outputId": "7bd16b1f-bd2e-486b-b963-9733aaa757be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=1, out_features=10, bias=True)\n",
       "  (1): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (2): Linear(in_features=10, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same architecture as model_1 (but using nn.Sequential)\n",
    "model_2 = nn.Sequential(\n",
    "    nn.Linear(in_features=1, out_features=10),\n",
    "    nn.Linear(in_features=10, out_features=10),\n",
    "    nn.Linear(in_features=10, out_features=1)\n",
    ").to(device)\n",
    "\n",
    "model_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOtBAv1E7OqX"
   },
   "source": [
    "We'll setup the loss function to be `nn.L1Loss()` (the same as mean absolute error) and the optimizer to be `torch.optim.SGD()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "f06fraapHC8E"
   },
   "outputs": [],
   "source": [
    "# Loss and optimizer\n",
    "loss_fn = nn.L1Loss()\n",
    "optimizer = torch.optim.SGD(model_2.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lU7GfFLm7a21"
   },
   "source": [
    "Now let's train the model using the regular training loop steps for `epochs=1000` (just like `model_1`).\n",
    "\n",
    "> **Note:** We've been writing similar training loop code over and over again. I've made it that way on purpose though, to keep practicing. However, do you have ideas how we could functionize this? That would save a fair bit of coding in the future. Potentially there could be a function for training and a function for testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YTitEWgSHC8E",
    "outputId": "16da5efa-3c3b-494b-ef4e-e5244f4cf097"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 0.75986, Test loss: 0.54143\n",
      "Epoch: 100 | Train loss: 0.09309, Test loss: 0.02901\n",
      "Epoch: 200 | Train loss: 0.07376, Test loss: 0.02850\n",
      "Epoch: 300 | Train loss: 0.06745, Test loss: 0.00615\n",
      "Epoch: 400 | Train loss: 0.06107, Test loss: 0.02004\n",
      "Epoch: 500 | Train loss: 0.05698, Test loss: 0.01061\n",
      "Epoch: 600 | Train loss: 0.04857, Test loss: 0.01326\n",
      "Epoch: 700 | Train loss: 0.06109, Test loss: 0.02127\n",
      "Epoch: 800 | Train loss: 0.05599, Test loss: 0.01426\n",
      "Epoch: 900 | Train loss: 0.05571, Test loss: 0.00603\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epochs\n",
    "epochs = 1000\n",
    "\n",
    "# Put data to target device\n",
    "X_train_regression, y_train_regression = X_train_regression.to(device), y_train_regression.to(device)\n",
    "X_test_regression, y_test_regression = X_test_regression.to(device), y_test_regression.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training \n",
    "    # 1. Forward pass\n",
    "    y_pred = model_2(X_train_regression)\n",
    "    \n",
    "    # 2. Calculate loss (no accuracy since it's a regression problem, not classification)\n",
    "    loss = loss_fn(y_pred, y_train_regression)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_2.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_pred = model_2(X_test_regression)\n",
    "      # 2. Calculate the loss \n",
    "      test_loss = loss_fn(test_pred, y_test_regression)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 100 == 0: \n",
    "        print(f\"Epoch: {epoch} | Train loss: {loss:.5f}, Test loss: {test_loss:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IoyLsZW78m_6"
   },
   "source": [
    "Okay, unlike `model_1` on the classification data, it looks like `model_2`'s loss is actually going down.\n",
    "\n",
    "Let's plot its predictions to see if that's so.\n",
    "\n",
    "And remember, since our model and data are using the target `device`, and this device may be a GPU, however, our plotting function uses matplotlib and matplotlib can't handle data on the GPU.\n",
    "\n",
    "To handle that, we'll send all of our data to the CPU using [`.cpu()`](https://pytorch.org/docs/stable/generated/torch.Tensor.cpu.html) when we pass it to `plot_predictions()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "AvltMCW_HC8E",
    "outputId": "5887db7d-128b-46f3-c978-c12af112d470"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Turn on evaluation mode\n",
    "model_2.eval()\n",
    "\n",
    "# Make predictions (inference)\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_2(X_test_regression)\n",
    "\n",
    "# Plot data and predictions with data on the CPU (matplotlib can't handle data on the GPU)\n",
    "# (try removing .cpu() from one of the below and see what happens)\n",
    "plot_predictions(train_data=X_train_regression.cpu(),\n",
    "                 train_labels=y_train_regression.cpu(),\n",
    "                 test_data=X_test_regression.cpu(),\n",
    "                 test_labels=y_test_regression.cpu(),\n",
    "                 predictions=y_preds.cpu());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cZFiXR8B9wYx"
   },
   "source": [
    "Alright, it looks like our model is able to do far better than random guessing on straight lines.\n",
    "\n",
    "This is a good thing.\n",
    "\n",
    "It means our model at least has *some* capacity to learn.\n",
    "\n",
    "> **Note:** A helpful troubleshooting step when building deep learning models is to start as small as possible to see if the model works before scaling it up. \n",
    ">\n",
    "> This could mean starting with a simple neural network (not many layers, not many hidden neurons) and a small dataset (like the one we've made) and then **overfitting** (making the model perform too well) on that small example before increasing the amount of data or the model size/design to *reduce* overfitting.\n",
    "\n",
    "So what could it be?\n",
    "\n",
    "Let's find out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j82n3OyWHC8E"
   },
   "source": [
    "## 6. The missing piece: non-linearity \n",
    "\n",
    "We've seen our model can draw straight (linear) lines, thanks to its linear layers.\n",
    "\n",
    "But how about we give it the capacity to draw non-straight (non-linear) lines?\n",
    "\n",
    "How?\n",
    "\n",
    "Let's find out.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cmfOV8v6__17"
   },
   "source": [
    "### 6.1 Recreating non-linear data (red and blue circles)\n",
    "\n",
    "First, let's recreate the data to start off fresh. We'll use the same setup as before. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "owqilKGBHC8F",
    "outputId": "b8c1d692-6f3e-43ca-9323-98bd40e39a89"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make and plot data\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_circles\n",
    "\n",
    "n_samples = 1000\n",
    "\n",
    "X, y = make_circles(n_samples=1000,\n",
    "    noise=0.03,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D63mlmxR_pxt"
   },
   "source": [
    "Nice! Now let's split it into training and test sets using 80% of the data for training and 20% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0UkH4cKLHC8F",
    "outputId": "5619ad2f-ad5d-476a-dcfd-9f8d48036899"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.6579, -0.4651],\n",
       "         [ 0.6319, -0.7347],\n",
       "         [-1.0086, -0.1240],\n",
       "         [-0.9666, -0.2256],\n",
       "         [-0.1666,  0.7994]]),\n",
       " tensor([1., 0., 0., 0., 1.]))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to tensors and split into train and test sets\n",
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Turn data into tensors\n",
    "X = torch.from_numpy(X).type(torch.float)\n",
    "y = torch.from_numpy(y).type(torch.float)\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42\n",
    ")\n",
    "\n",
    "X_train[:5], y_train[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wNm_OBgD_4tk"
   },
   "source": [
    "### 6.2 Building a model with non-linearity \n",
    "\n",
    "Now here comes the fun part.\n",
    "\n",
    "What kind of pattern do you think you could draw with unlimited straight (linear) and non-straight (non-linear) lines?\n",
    "\n",
    "I bet you could get pretty creative.\n",
    "\n",
    "So far our neural networks have only been using linear (straight) line functions.\n",
    "\n",
    "But the data we've been working with is non-linear (circles).\n",
    "\n",
    "What do you think will happen when we introduce the capability for our model to use **non-linear activation functions**?\n",
    "\n",
    "Well let's see.\n",
    "\n",
    "PyTorch has a bunch of [ready-made non-linear activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) that do similar but different things. \n",
    "\n",
    "One of the most common and best performing is [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) (rectified linear-unit, [`torch.nn.ReLU()`](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html)).\n",
    "\n",
    "Rather than talk about it, let's put it in our neural network between the hidden layers in the forward pass and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i_yG7AFHHC8F",
    "outputId": "00a69e38-369f-47fb-b729-fd4c7b5b47de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CircleModelV2(\n",
      "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
      "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
      "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
      "  (relu): ReLU()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Build model with non-linear activation function\n",
    "from torch import nn\n",
    "class CircleModelV2(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=10)\n",
    "        self.layer_2 = nn.Linear(in_features=10, out_features=10)\n",
    "        self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
    "        self.relu = nn.ReLU() # <- add in ReLU activation function\n",
    "        # Can also put sigmoid in the model \n",
    "        # This would mean you don't need to use it on the predictions\n",
    "        # self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "    def forward(self, x):\n",
    "      # Intersperse the ReLU activation function between layers\n",
    "       return self.layer_3(self.relu(self.layer_2(self.relu(self.layer_1(x)))))\n",
    "\n",
    "model_3 = CircleModelV2().to(device)\n",
    "print(model_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1UASf5SWEPNJ"
   },
   "source": [
    "![a classification neural network on TensorFlow playground with ReLU activation](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-tensorflow-playground-relu-activation.png)\n",
    "*A visual example of what a similar classification neural network to the one we've just built (using ReLU activation) looks like. Try creating one of your own on the [TensorFlow Playground website](https://playground.tensorflow.org/).*\n",
    "\n",
    "> **Question:** *Where should I put the non-linear activation functions when constructing a neural network?*\n",
    ">\n",
    "> A rule of thumb is to put them in between hidden layers and just after the output layer, however, there is no set in stone option. As you learn more about neural networks and deep learning you'll find a bunch of different ways of putting things together. In the meantime, best to experiment, experiment, experiment.\n",
    "\n",
    "Now we've got a model ready to go, let's create a binary classification loss function as well as an optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "uWNlx4lTHC8F"
   },
   "outputs": [],
   "source": [
    "# Setup loss and optimizer \n",
    "loss_fn = nn.BCEWithLogitsLoss()\n",
    "optimizer = torch.optim.SGD(model_3.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NQL9GF5yFTGD"
   },
   "source": [
    "Wonderful! \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "INnCmr2RMk8L"
   },
   "source": [
    "### 6.3 Training a model with non-linearity\n",
    "\n",
    "You know the drill, model, loss function, optimizer ready to go, let's create a training and testing loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gTS8yTN_HC8F",
    "outputId": "df71bd5d-cd0d-4e39-f3e0-7fc90bfaaeb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.69295, Accuracy: 50.00% | Test Loss: 0.69319, Test Accuracy: 50.00%\n",
      "Epoch: 100 | Loss: 0.69115, Accuracy: 52.88% | Test Loss: 0.69102, Test Accuracy: 52.50%\n",
      "Epoch: 200 | Loss: 0.68977, Accuracy: 53.37% | Test Loss: 0.68940, Test Accuracy: 55.00%\n",
      "Epoch: 300 | Loss: 0.68795, Accuracy: 53.00% | Test Loss: 0.68723, Test Accuracy: 56.00%\n",
      "Epoch: 400 | Loss: 0.68517, Accuracy: 52.75% | Test Loss: 0.68411, Test Accuracy: 56.50%\n",
      "Epoch: 500 | Loss: 0.68102, Accuracy: 52.75% | Test Loss: 0.67941, Test Accuracy: 56.50%\n",
      "Epoch: 600 | Loss: 0.67515, Accuracy: 54.50% | Test Loss: 0.67285, Test Accuracy: 56.00%\n",
      "Epoch: 700 | Loss: 0.66659, Accuracy: 58.38% | Test Loss: 0.66322, Test Accuracy: 59.00%\n",
      "Epoch: 800 | Loss: 0.65160, Accuracy: 64.00% | Test Loss: 0.64757, Test Accuracy: 67.50%\n",
      "Epoch: 900 | Loss: 0.62362, Accuracy: 74.00% | Test Loss: 0.62145, Test Accuracy: 79.00%\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "torch.manual_seed(42)\n",
    "epochs = 1000\n",
    "\n",
    "# Put all data on target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_3(X_train).squeeze()\n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "    \n",
    "    # 2. Calculate loss and accuracy\n",
    "    loss = loss_fn(y_logits, y_train) # BCEWithLogitsLoss calculates loss using logits\n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred)\n",
    "    \n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backward\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_3.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_logits = model_3(X_test).squeeze()\n",
    "      test_pred = torch.round(torch.sigmoid(test_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "      # 2. Calculate loss and accuracy\n",
    "      test_loss = loss_fn(test_logits, y_test)\n",
    "      test_acc = accuracy_fn(y_true=y_test,\n",
    "                             y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 100 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test Loss: {test_loss:.5f}, Test Accuracy: {test_acc:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x89XvV-EMqvB"
   },
   "source": [
    "Ho ho! That's looking far better!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tfViHC1aM15t"
   },
   "source": [
    "### 6.4 Evaluating a model trained with non-linear activation functions\n",
    "\n",
    "Remember how our circle data is non-linear? Well, let's see how our models predictions look now the model's been trained with non-linear activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wGHQiWT2HC8G",
    "outputId": "e95a412b-bef1-4d3d-b705-1eb75a55449e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1., 0., 1., 0., 0., 1., 0., 0., 1., 0.], device='cuda:0'),\n",
       " tensor([1., 1., 1., 1., 0., 1., 1., 1., 1., 0.]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "model_3.eval()\n",
    "with torch.inference_mode():\n",
    "    y_preds = torch.round(torch.sigmoid(model_3(X_test))).squeeze()\n",
    "y_preds[:10], y[:10] # want preds in same format as truth labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "gEeMinjyHC8G",
    "outputId": "3566e274-ef7a-4eb8-ef34-57b97e7781bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_1, X_train, y_train) # model_1 = no non-linearity\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_3, X_test, y_test) # model_3 = has non-linearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cAfU5dXrNGuC"
   },
   "source": [
    "Nice! Not perfect but still far better than before.\n",
    "\n",
    "Potentially you could try a few tricks to improve the test accuracy of the model? (hint: head back to section 5 for tips on improving the model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rarkXnX-Nhj2"
   },
   "source": [
    "## 7. Replicating non-linear activation functions\n",
    "\n",
    "We saw before how adding non-linear activation functions to our model can help it to model non-linear data.\n",
    "\n",
    "> **Note:** Much of the data you'll encounter in the wild is non-linear (or a combination of linear and non-linear). Right now we've been working with dots on a 2D plot. But imagine if you had images of plants you'd like to classify, there's a lot of different plant shapes. Or text from Wikipedia you'd like to summarize, there's lots of different ways words can be put together (linear and non-linear patterns). \n",
    "\n",
    "But what does a non-linear activation *look* like?\n",
    "\n",
    "How about we replicate some and what they do?\n",
    "\n",
    "Let's start by creating a small amount of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uvpqD28OscTm",
    "outputId": "befa3534-f5b1-48f2-88c5-63836759d834"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,\n",
       "          2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a toy tensor (similar to the data going into our model(s))\n",
    "A = torch.arange(-10, 10, 1, dtype=torch.float32)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vvSZ4M3-ssZn"
   },
   "source": [
    "Wonderful, now let's plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "FTCID1lRsrte",
    "outputId": "477fdd3d-ae28-4caf-b9ed-b2a9ec369bee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the toy tensor\n",
    "plt.plot(A);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ovyXy-Qxsx9x"
   },
   "source": [
    "A straight line, nice.\n",
    "\n",
    "Now let's see how the ReLU activation function influences it.\n",
    "\n",
    "And instead of using PyTorch's ReLU (`torch.nn.ReLU`), we'll recreate it ourselves.\n",
    "\n",
    "The ReLU function turns all negatives to 0 and leaves the positive values as they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XhdCQKbcsxi1",
    "outputId": "47c7fa54-6d03-47b7-a745-0bb3599c82fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6., 7.,\n",
       "        8., 9.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create ReLU function by hand \n",
    "def relu(x):\n",
    "  return torch.maximum(torch.tensor(0), x) # inputs must be tensors\n",
    "\n",
    "# Pass toy tensor through ReLU function\n",
    "relu(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dqw_P9GiuiB9"
   },
   "source": [
    "It looks like our ReLU function worked, all of the negative values are zeros.\n",
    "\n",
    "Let's plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "fICVVmAgsxal",
    "outputId": "4ada6523-81e8-450e-89b5-0be3da23f04c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbEElEQVR4nO3deXiU1aHH8e8hCyEQCIGw7/smEIwIaN3rglatK1hvteKl2gsIinuLWm9vXapFqbfWWm3rxbCraNW6a92oQBIIhH0Na9hCCIQsc+4fGfukMSETMu+cWX6f58nDJO+bzO85TH5zcjInr7HWIiIi4auJ6wAiInJiKmoRkTCnohYRCXMqahGRMKeiFhEJc/FefNG2bdvaHj16ePGlRUSi0rJly/ZZa9NrO+ZJUffo0YOlS5d68aVFRKKSMWZrXce09CEiEuZU1CIiYU5FLSIS5lTUIiJhTkUtIhLmVNQiImFORS0iEuZU1CIiQfD1pv386fPNePGno1XUIiKNdLCkjKlzcpj99VaOlVcG/eurqEVEGsFay90LVnCgpIxnx2eQnBj8Dd8qahGRRvjrV1v5IH8P914ygCGdW3lyHypqEZGTtHrnYX71dj7nDWjHLWf08Ox+VNQiIifhaFkFk7OWk9osgSevGYoxxrP78uSv54mIRLtHFq9m074SZk84nTYtmnp6X5pRi4g00Ju5O5m7dDs/O6c3Y/q09fz+VNQiIg2w/cBRHli0khHdUpl6Qb+Q3KeKWkQkQOWVPiZnZYOBZ8ZlkBAXmgrVGrWISICefn8dOdsP8dwNI+ialhyy+9WMWkQkAJ+v38fzn25k/MiuXDq0Y0jvW0UtIlKPfUeOM21eDr3TWzDjssEhv38tfYiInIDPZ5k+P5eiY+W8MmEkzRLjQp5BM2oRkRN46YvNfLK2kF9cOpABHVo6yaCiFhGpw8qCIh5/dw0XDmrPjaO6O8uhohYRqcWR41VbxNu2aMoTHm8Rr4/WqEVEajHj9Ty2HTjKnImjSU1OdJpFM2oRkRoWLS9gUfYOppzfl5E901zHUVGLiFS3eV8Jv3g9j5E905h8Xl/XcYAAi9oYM80Ys8oYk2eMyTLGJHkdTEQk1MoqfEzJyiY+rgkzrx9OXBN369LV1VvUxpjOwBQg01o7BIgDxnkdTEQk1J54dw0rdxTx5DVD6ZTazHWcfwl06SMeaGaMiQeSgZ3eRRIRCb2P1+7lxc838+PR3blwcAfXcf5NvUVtrd0B/AbYBuwCiqy179U8zxgz0Riz1BiztLCwMPhJRUQ8svdwKdPn5TKgQwoPjB3oOs53BLL00Rq4AugJdAKaG2NurHmetfYFa22mtTYzPT09+ElFRDzg81nunJdLSVkFv7shg6SE0G8Rr08gSx8XAJuttYXW2nJgETDG21giIqHxh8828fmGfTz8g8H0aZfiOk6tAinqbcAoY0yyqdqacz6Q720sERHvZW87yFPvreXSoR25/rSuruPUKZA16iXAAmA5sNL/OS94nEtExFOHS8uZnJVN+5ZJ/M8PT3G6Rbw+AW0ht9Y+BDzkcRYRkZCw1vLAopXsKipl3k9H06pZgutIJ6SdiSISc+YvLeCtFbu48/v9OLV7a9dx6qWiFpGYsmFvMQ8tXsWY3m247ezeruMEREUtIjGjtLySSa9m0ywxjt+G0Rbx+ujPnIpIzHjsnTWs2V3MyzefRvuWkfMnizSjFpGY8P7qPfz5yy1MOLMn5w5o5zpOg6ioRSTq7So6xt0LchncqSX3XNzfdZwGU1GLSFSr9FmmzsmhrMLHrPEZNI0Pvy3i9dEatYhEtec+3sCSzQd46tph9Epv4TrOSdGMWkSi1jdbDjDzg3VcObwTV43o7DrOSVNRi0hUOnS0jDuysumalsx/h/kW8fpo6UNEoo61lvsWrmRv8XEW/WwMLZpGdtVpRi0iUWf2km28u2o391zcn6FdUl3HaTQVtYhElbW7i3n0rdWc1S+dW8/s5TpOUKioRSRqHCurZHLWclKSEnjq2mE0iZAt4vWJ7IUbEZFqHv3batbtOcIrE0aSntLUdZyg0YxaRKLCOyt38eqSbfz07F58r290XbdVRS0iEa/g4FHuXbiCYV1TmX5h5G0Rr4+KWkQiWkWljzvm5OCzMGtcBglx0VdrWqMWkYj2zIfrWbb1IM+MG063Nsmu43gi+p56RCRmfLlxH7/7eAPXntqFK4ZH7hbx+qioRSQiHSgpY9rcHHq2bc4jVwx2HcdTKmoRiTjWWu6en8vBknJmjc8gOTG6V3FV1CIScf785RY+XLOX+8cOYHCnVq7jeE5FLSIRJW9HEb9+ew3nD2jHzWN6uI4TEipqEYkYJccrmJKVTevmCTx57bCI/tOlDRHdCzsiElUeXryKzftLmH3r6aQ1T3QdJ2Q0oxaRiPBGzg7mLytg0rl9GNO7res4IaWiFpGwt3V/CQ++lsep3Vtzx/l9XccJORW1iIS1sgofU7KyaWLgmXHDiY/CLeL10Rq1iIS1p95fS25BEf/7oxF0aR2dW8TrE3tPTSISMT5bV8gfPt3E+JHdGHtKR9dxnFFRi0hYKiw+zp3zcunXvgUzLhvkOo5TWvoQkbDj81nump9LcWk5s289nWaJca4jOaUZtYiEnRc/38Rn6wr5+WWD6N8hxXUc51TUIhJWcrcf4ol313LR4PbceHo313HCQkBFbYxJNcYsMMasMcbkG2NGex1MRGJPcWk5k7OyaZfSlMevHhozW8TrE+ga9TPAu9baa4wxiUBsvkZGRDxjreXnr+dRcPAocyaOJjU5draI16feojbGtATOAm4GsNaWAWXexhKRWLNw+Q7eyNnJtAv6MbJnmus4YSWQpY9eQCHwsjEm2xjzojGmec2TjDETjTFLjTFLCwsLgx5URKLXpsIjzHgjj5E905h0Xh/XccJOIEUdD4wAfm+tzQBKgPtqnmStfcFam2mtzUxPTw9yTBGJVscrKpmclU1ifBOeGTecuCZal64pkKIuAAqstUv87y+gqrhFRBrt8XfWsmrnYZ64eigdWzVzHScs1VvU1trdwHZjTH//h84HVnuaSkRiwkdr9vDSF5u5aXR3LhzcwXWcsBXoqz4mA7P9r/jYBPzEu0giEgv2HC5l+vwVDOiQwv1jB7qOE9YCKmprbQ6Q6W0UEYkVlT7L1Dk5HCur5Hc3ZJCUENtbxOujv/UhIiH3/Kcb+WrTfh6/+hT6tNMW8fpoC7mIhNSyrQd4+v11XDa0I9dldnUdJyKoqEUkZIqOlTMlK4eOrZL4n6tO0RbxAGnpQ0RCwlrLA4tWsvtwKfNvG03LpATXkSKGZtQiEhJzvtnO31bu4q4L+zGiW2vXcSKKilpEPLd+TzGPvLmKM/u05bazeruOE3FU1CLiqdLyqi3izRPjefq6YTTRFvEG0xq1iHjqV3/LZ83uYl7+yWm0a5nkOk5E0oxaRDzzbt5uXvl6K7ee2ZNz+7dzHSdiqahFxBM7Dx3j3oUrOKVzK+65eIDrOBFNRS0iQVdR6WPqnBwqKn08Oz6DxHhVTWNojVpEgm7WRxv455YDPH3dMHq2/c51RqSB9DQnIkH19ab9zPpoPVdldOaqEV1cx4kKKmoRCZqDJWVMm5tDt7RkfnnlENdxooaWPkQkKKy13LNwBfuOHGfR7WfQoqnqJVg0oxaRoHjl6628v3oP9148gFO6tHIdJ6qoqEWk0fJ3Hea//5bPOf3TueWMnq7jRB0VtYg0ytGyCiZnZdOqWQK/uVZbxL2gRSQRaZRfvrmajYVHeOWW02nboqnrOFFJM2oROWlvrdjJnG+2c9vZvTmzb1vXcaKWilpETsr2A0e5f9FKhndN5c7v93MdJ6qpqEWkwcorfUyZkw0WZo3PICFOVeIlrVGLSIPN/GAd2dsOMWt8Bl3Tkl3HiXp6GhSRBvlywz7+95ONXJ/ZlR8M6+Q6TkxQUYtIwPYfOc7UuTn0atuchy4f5DpOzNDSh4gExFrL9Pm5HDpWzp9/MpLkRNVHqGhGLSIBeemLLXy8tpAHxw5kUKeWruPEFBW1iNQrb0cRj72TzwUD2/Pj0d1dx4k5KmoROaGS41VbxNs0b8qT1wzFGG0RDzUtMonICc14YxVb95fw6n+OonXzRNdxYpJm1CJSp9ezd7BweQGTzuvLqF5tXMeJWSpqEanV1v0lPPjaSk7r0Zop5/VxHSemqahF5DvKKnxMzsomPq4JM8dlEK8t4k5pjVpEvuM3761lRUERz994Kp1Tm7mOE/P0NCki/+bTdYW88NkmbhzVjYuHdHAdR2hAURtj4owx2caYt7wMJCLu7C0u5a55OfRvn8LPL9UW8XDRkBn1HUC+V0FExC2fz3LXvFyOHK9g1g0ZJCXEuY4kfgEVtTGmC3Ap8KK3cUTElT/+YxP/WL+PGZcNpl/7FNdxpJpAZ9QzgXsAX10nGGMmGmOWGmOWFhYWBiObiIRIzvZDPPn3tVwypAPjR3Z1HUdqqLeojTGXAXuttctOdJ619gVrbaa1NjM9PT1oAUXEW8Wl5UzJyqZ9yyQeu0pbxMNRIDPqM4DLjTFbgDnAecaY//M0lYiEhLWWB1/LY8ehYzw7fjitkhNcR5Ja1FvU1tr7rbVdrLU9gHHAR9baGz1PJiKeW7CsgMW5O5l2QV9O7Z7mOo7UQa+jFolRGwuPMOONVYzqlcbt52iLeDhr0M5Ea+0nwCeeJBGRkDleUcnkV7NJSmjCzOsziGuidelwpi3kIjHosXfWsHrXYf50UyYdWiW5jiP10NKHSIz5MH8PL3+xhZvH9OD8ge1dx5EAqKhFYsjuolKmz89lUMeW3D92gOs4EiAVtUiMqPRZps7NprTcx6wbMmgary3ikUJr1CIx4vefbODrTQd44pqh9E5v4TqONIBm1CIxYNnWA/z2g/VcPqwT157axXUcaSAVtUiUKzpazpSsHDqnNuNXPxyiLeIRSEsfIlHMWst9i1aw53ApC24fQ0qStohHIs2oRaJY1j+3807ebqZf1J/hXVNdx5GTpKIWiVLr9hTzyJur+F7ftkz8Xi/XcaQRVNQiUai0vJJJry4nJSmep64bRhNtEY9oWqMWiUKPvrWadXuO8JdbRtIuRVvEI51m1CJR5t28Xcxeso2JZ/Xi7H66iEc0UFGLRJEdh45xz4IVDOvSiukX9ncdR4JERS0SJSoqfdyRlY3PwrPjM0iM17d3tNAatUiUePbD9SzdepCZ1w+ne5vmruNIEOkpVyQKfLVxP7M+3sDVI7pwZUZn13EkyFTUIhHuYEkZ0+bm0KNNc355xWDXccQDWvoQiWDWWu5ekMv+kuO8dtMZNG+qb+lopBm1SAT761db+SB/L/ddMpAhnVu5jiMeUVGLRKjVOw/zq7fzOW9AO245o4frOOIhFbVIBDpaVsGkrOWkNkvgyWuG6k+XRjktaIlEoEcWr2bzvhJmTzidNi2auo4jHtOMWiTCvJm7k7lLt/Ozc3ozpk9b13EkBFTUIhFk+4GjPLBoJSO6pTL1gn6u40iIqKhFIkR5pY/JWdlg4JlxGSTE6ds3VmiNWiRCPP3+OnK2H+K5G0bQNS3ZdRwJIT0li0SAz9fv4/lPNzJ+ZFcuHdrRdRwJMRW1SJjbd+Q40+bl0Ce9BTMu0xbxWKSlD5Ew5vNZ7pqXS9Gxcl6ZMJJmiXGuI4kDmlGLhLGXvtjMp+sK+cWlAxnQoaXrOOKIilokTK0sKOLxd9dw0eD23Diqu+s44pCKWiQMHTleweSs5aS3aMrjV2uLeKzTGrVIGJrxeh7bDhxlzsTRpCYnuo4jjmlGLRJmFi0vYFH2Dqac35eRPdNcx5EwUG9RG2O6GmM+NsbkG2NWGWPuCEUwkVi0eV8JP389j5E905h8Xl/XcSRMBLL0UQHcZa1dboxJAZYZY9631q72OJtITCmr8DE5azmJ8U14Ztxw4ppoXVqq1DujttbustYu998uBvIBXT1TJMieeHcNeTsO88TVQ+nYqpnrOBJGGrRGbYzpAWQAS2o5NtEYs9QYs7SwsDBI8URiw8dr9/Li55v58ejuXDi4g+s4EmYCLmpjTAtgITDVWnu45nFr7QvW2kxrbWZ6enowM4pEtb2HS5k+L5cBHVJ4YOxA13EkDAX08jxjTAJVJT3bWrvI20giscPns0ybl0NJWQVzbxhFUoK2iMt31VvUpuqV9n8C8q21T3sfSSR2PP/ZRr7YsJ/HrjqFPu1SXMeRMBXI0scZwH8A5xljcvxvYz3OJRL1lm87yFPvrePSoR25/rSuruNIGKt3Rm2t/RzQ64REguhwaTlTsrLp2CqJX191iraIywlpC7lIiFlreWDRSnYVlTL/ttG0TEpwHUnCnLaQi4TYvKXbeWvFLu78fj9GdGvtOo5EABW1SAht2FvMw4tXc0afNtx+dm/XcSRCqKhFQqS0vJJJr2bTLDGO3143nCbaIi4B0hq1SIj8+u181uwu5uWbT6NdyyTXcSSCaEYtEgLvrdrNX77ayoQze3LugHau40iEUVGLeGxX0THuWbiCIZ1bcs/F/V3HkQikohbxUKXPMnVODmUVPp4dl0HTeG0Rl4bTGrWIh3730QaWbD7AU9cOo1d6C9dxJEJpRi3ikW+2HOCZD9fxw4zOXH1qF9dxJIKpqEU8cOhoGXdkZdMtLZlHrxziOo5EOC19iASZtZZ7F66g8MhxFt4+hhZN9W0mjaMZtUiQ/d+Sbfx91R7uuWgAQ7ukuo4jUUBFLRJEa3Yf5tG3VnN2v3QmnNnTdRyJEipqkSA5VlbJ5FezaZmUwG+uHaYt4hI0WjwTCZJfvrWa9XuP8MqEkaSnNHUdR6KIZtQiQfD2yl1k/XMbt53dm+/11cWdJbhU1CKNVHDwKPctXMGwrqncdWE/13EkCqmoRRqhotLHHXNysBZmjcsgIU7fUhJ8WqMWaYSZH6xn2daDPDs+g25tkl3HkSilp3+Rk/Tlxn0898kGrsvswuXDOrmOI1FMRS1yEg6UlDFtbg492zbn4csHu44jUU5FLdJA1lrunp/LwZJyZo3PIDlRK4jiLRW1SAO9/MUWPlyzlwfGDmBwp1au40gMUFGLNEDejiIee2cNFwxsx01jeriOIzFCRS0SoJLjFUzJyqZ18wSeuGYYxmiLuISGFtdEAvTQ4lVs3l/Cq7eOIq15ous4EkM0oxYJwBs5O1iwrIDJ5/ZhdO82ruNIjFFRi9Rj6/4SHnwtj8zurZlyfl/XcSQGqahFTqCswseUrGyaGJg5bjjx2iIuDmiNWuQEnnpvLbkFRfz+RyPo0lpbxMUNTQ9E6vDZukL+8Nkmbji9G5ec0tF1HIlhKmqRWhQWH+fOebn0a9+CGZcNch1HYpyWPkRq8Pksd87Lobi0nFf/83SSEuJcR5IYpxm1SA1//Mcm/rF+HzN+MIh+7VNcxxEJrKiNMRcbY9YaYzYYY+7zOpSIK7nbD/Hk39dyyZAO3DCym+s4IkAARW2MiQOeAy4BBgHjjTFatJOoU1xazuSsbNq3TOKxq4Zqi7iEjUDWqEcCG6y1mwCMMXOAK4DVwQ7zg1mfU1peGewvKxKQ4tIK9haXMu+no2mVnOA6jsi/BFLUnYHt1d4vAE6veZIxZiIwEaBbt5P7kbF3enPKKn0n9bkiwXDR4A5k9khzHUPk3wRS1LX9/Ge/8wFrXwBeAMjMzPzO8UDMHJdxMp8mIhLVAvllYgHQtdr7XYCd3sQREZGaAinqb4C+xpiexphEYByw2NtYIiLyrXqXPqy1FcaYScDfgTjgJWvtKs+TiYgIEODORGvt28DbHmcREZFaaGeiiEiYU1GLiIQ5FbWISJhTUYuIhDlj7UntTTnxFzWmENh6kp/eFtgXxDjBpnyNo3yNo3yNE875ultr02s74ElRN4YxZqm1NtN1jrooX+MoX+MoX+OEe766aOlDRCTMqahFRMJcOBb1C64D1EP5Gkf5Gkf5Gifc89Uq7NaoRUTk34XjjFpERKpRUYuIhDknRV3fxXJNlWf9x1cYY0aEOF9XY8zHxph8Y8wqY8wdtZxzjjGmyBiT43+bEeKMW4wxK/33vbSW487G0BjTv9q45BhjDhtjptY4J6TjZ4x5yRiz1xiTV+1jacaY940x6/3/tq7jcz2/uHMd+Z40xqzx//+9ZoxJreNzT/hY8DDfw8aYHdX+D8fW8bmuxm9utWxbjDE5dXyu5+PXaNbakL5R9adSNwK9gEQgFxhU45yxwDtUXV1mFLAkxBk7AiP8t1OAdbVkPAd4K9TjV+3+twBtT3Dc6RjW+P/eTdWL+Z2NH3AWMALIq/axJ4D7/LfvAx6vI/8JH68e5rsQiPfffry2fIE8FjzM9zAwPYD/fyfjV+P4U8AMV+PX2DcXM+p/XSzXWlsGfHux3OquAP5qq3wNpBpjOoYqoLV2l7V2uf92MZBP1bUjI4nTMazmfGCjtfZkd6oGhbX2M+BAjQ9fAfzFf/svwJW1fGogj1dP8llr37PWVvjf/Zqqqys5Ucf4BcLZ+H3LVF1O/jogK9j3Gyouirq2i+XWLMFAzgkJY0wPIANYUsvh0caYXGPMO8aYwaFNhgXeM8Ys819YuKZwGcNx1P0N4nL8ANpba3dB1ZMz0K6Wc8JlHG+h6iek2tT3WPDSJP/SzEt1LB2Fw/h9D9hjrV1fx3GX4xcQF0UdyMVyA7qgrteMMS2AhcBUa+3hGoeXU/Xj/DBgFvB6iOOdYa0dAVwC/Jcx5qwax52Poam6dNvlwPxaDrsev0CFwzg+CFQAs+s4pb7Hgld+D/QGhgO7qFpeqMn5+AHjOfFs2tX4BcxFUQdysVznF9Q1xiRQVdKzrbWLah631h621h7x334bSDDGtA1VPmvtTv+/e4HXqPoRszrnY0jVA3+5tXZPzQOux89vz7fLQf5/99ZyjtNxNMbcBFwG/Mj6F1RrCuCx4Alr7R5rbaW11gf8sY77dT1+8cBVwNy6znE1fg3hoqgDuVjuYuDH/lcujAKKvv0RNRT8a1p/AvKttU/XcU4H/3kYY0ZSNZb7Q5SvuTEm5dvbVP3SKa/GaU7H0K/OmYzL8atmMXCT//ZNwBu1nOPs4s7GmIuBe4HLrbVH6zgnkMeCV/mq/87jh3Xcr+uLY18ArLHWFtR20OX4NYiL32BS9YqEdVT9NvhB/8duA27z3zbAc/7jK4HMEOc7k6ofz1YAOf63sTUyTgJWUfVb7K+BMSHM18t/v7n+DOE4hslUFW+rah9zNn5UPWHsAsqpmuVNANoAHwLr/f+m+c/tBLx9osdriPJtoGp999vH4PM189X1WAhRvlf8j60VVJVvx3AaP//H//ztY67auSEfv8a+aQu5iEiY085EEZEwp6IWEQlzKmoRkTCnohYRCXMqahGRMKeiFhEJcypqEZEw9//DjZh6Lhvu5AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ReLU activated toy tensor\n",
    "plt.plot(relu(A));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AlmI4CHzsxEt"
   },
   "source": [
    "Nice! That looks exactly like the shape of the ReLU function on the [Wikipedia page for ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)).\n",
    "\n",
    "How about we try the [sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) we've been using?\n",
    "\n",
    "The sigmoid function formula goes like so:\n",
    "\n",
    "$$ out_i = \\frac{1}{1+e^{-input_i}} $$ \n",
    "\n",
    "Or using $x$ as input:\n",
    "\n",
    "$$ S(x) = \\frac{1}{1+e^{-x_i}} $$\n",
    "\n",
    "Where $S$ stands for sigmoid, $e$ stands for [exponential](https://en.wikipedia.org/wiki/Exponential_function) ([`torch.exp()`](https://pytorch.org/docs/stable/generated/torch.exp.html)) and $i$ stands for a particular element in a tensor.\n",
    "\n",
    "Let's build a function to replicate the sigmoid function with PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hcDQDy8bvDcR",
    "outputId": "899e7595-5b1c-4182-c1ca-94aadaa097e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4.5398e-05, 1.2339e-04, 3.3535e-04, 9.1105e-04, 2.4726e-03, 6.6929e-03,\n",
       "        1.7986e-02, 4.7426e-02, 1.1920e-01, 2.6894e-01, 5.0000e-01, 7.3106e-01,\n",
       "        8.8080e-01, 9.5257e-01, 9.8201e-01, 9.9331e-01, 9.9753e-01, 9.9909e-01,\n",
       "        9.9966e-01, 9.9988e-01])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a custom sigmoid function\n",
    "def sigmoid(x):\n",
    "  return 1 / (1 + torch.exp(-x))\n",
    "\n",
    "# Test custom sigmoid on toy tensor\n",
    "sigmoid(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qiwvlDWmxPUt"
   },
   "source": [
    "Woah, those values look a lot like prediction probabilities we've seen earlier, let's see what they look like visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "dxihhxGBxOWf",
    "outputId": "c6a6d3de-e9fb-445d-8d63-3964753a4559"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot sigmoid activated toy tensor\n",
    "plt.plot(sigmoid(A));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IpOqVYpdxgWl"
   },
   "source": [
    "Looking good! We've gone from a straight line to a curved line.\n",
    "\n",
    "Now there's plenty more [non-linear activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) that exist in PyTorch that we haven't tried.\n",
    "\n",
    "But these two are two of the most common.\n",
    "\n",
    "And the point remains, what patterns could you draw using an unlimited amount of linear (straight) and non-linear (not straight) lines?\n",
    "\n",
    "Almost anything right?\n",
    "\n",
    "That's exactly what our model is doing when we combine linear and non-linear functions.\n",
    "\n",
    "Instead of telling our model what to do, we give it tools to figure out how to best discover patterns in the data.\n",
    "\n",
    "And those tools are linear and non-linear functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1OeaW0FHC8G"
   },
   "source": [
    "## 8. Putting things together by building a multi-class PyTorch model\n",
    "\n",
    "We've covered a fair bit.\n",
    "\n",
    "But now let's put it all together using a multi-class classification problem.\n",
    "\n",
    "Recall a **binary classification** problem deals with classifying something as one of two options (e.g. a photo as a cat photo or a dog photo) where as a **multi-class classification** problem deals with classifying something from a list of *more than* two options (e.g. classifying a photo as a cat a dog or a chicken).\n",
    "\n",
    "![binary vs multi-class classification image with the example of dog vs cat for binary classification and dog vs cat vs chicken for multi-class classification](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-binary-vs-multi-class-classification.png)\n",
    "*Example of binary vs. multi-class classification. Binary deals with two classes (one thing or another), where as multi-class classification can deal with any number of classes over two, for example, the popular [ImageNet-1k dataset](https://www.image-net.org/) is used as a computer vision benchmark and has 1000 classes.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f5Ephtx6f1jB"
   },
   "source": [
    "### 8.1 Creating multi-class classification data\n",
    "\n",
    "To begin a multi-class classification problem, let's create some multi-class data.\n",
    "\n",
    "To do so, we can leverage Scikit-Learn's [`make_blobs()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html) method.\n",
    "\n",
    "This method will create however many classes (using the `centers` parameter) we want.\n",
    "\n",
    "Specifically, let's do the following:\n",
    "\n",
    "1. Create some multi-class data with `make_blobs()`.\n",
    "2. Turn the data into tensors (the default of `make_blobs()` is to use NumPy arrays).\n",
    "3. Split the data into training and test sets using `train_test_split()`.\n",
    "4. Visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 515
    },
    "id": "x3_UwcwHHC8G",
    "outputId": "7cd92d66-41e3-4aa0-ab94-f9f9ef40fcce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-8.4134,  6.9352],\n",
      "        [-5.7665, -6.4312],\n",
      "        [-6.0421, -6.7661],\n",
      "        [ 3.9508,  0.6984],\n",
      "        [ 4.2505, -0.2815]]) tensor([3, 2, 2, 1, 1])\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Set the hyperparameters for data creation\n",
    "NUM_CLASSES = 4\n",
    "NUM_FEATURES = 2\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "# 1. Create multi-class data\n",
    "X_blob, y_blob = make_blobs(n_samples=1000,\n",
    "    n_features=NUM_FEATURES, # X features\n",
    "    centers=NUM_CLASSES, # y labels \n",
    "    cluster_std=1.5, # give the clusters a little shake up (try changing this to 1.0, the default)\n",
    "    random_state=RANDOM_SEED\n",
    ")\n",
    "\n",
    "# 2. Turn data into tensors\n",
    "X_blob = torch.from_numpy(X_blob).type(torch.float)\n",
    "y_blob = torch.from_numpy(y_blob).type(torch.LongTensor)\n",
    "print(X_blob[:5], y_blob[:5])\n",
    "\n",
    "# 3. Split into train and test sets\n",
    "X_blob_train, X_blob_test, y_blob_train, y_blob_test = train_test_split(X_blob,\n",
    "    y_blob,\n",
    "    test_size=0.2,\n",
    "    random_state=RANDOM_SEED\n",
    ")\n",
    "\n",
    "# 4. Plot data\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(X_blob[:, 0], X_blob[:, 1], c=y_blob, cmap=plt.cm.RdYlBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xBCnUs0oHC8G"
   },
   "source": [
    "Nice! Looks like we've got some multi-class data ready to go.\n",
    "\n",
    "Let's build a model to separate the coloured blobs. \n",
    "\n",
    "> **Question:** Does this dataset need non-linearity? Or could you draw a succession of straight lines to separate it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_dINSA0Chiof"
   },
   "source": [
    "### 8.2 Building a multi-class classification model in PyTorch\n",
    "\n",
    "We've created a few models in PyTorch so far.\n",
    "\n",
    "You might also be starting to get an idea of how flexible neural networks are.\n",
    "\n",
    "How about we build one similar to `model_3` but this is still capable of handling multi-class data?\n",
    "\n",
    "To do so, let's create a subclass of `nn.Module` that takes in three hyperparameters:\n",
    "* `input_features` - the number of `X` features coming into the model.\n",
    "* `output_features` - the ideal numbers of output features we'd like (this will be equivalent to `NUM_CLASSES` or the number of classes in your multi-class classification problem).\n",
    "* `hidden_units` - the number of hidden neurons we'd like each hidden layer to use.\n",
    "\n",
    "Since we're putting things together, let's setup some device agnostic code (we don't have to do this again in the same notebook, it's only a reminder).\n",
    "\n",
    "Then we'll create the model class using the hyperparameters above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "g9OPjDfQk1AU",
    "outputId": "64d275af-144c-4b5f-99b7-a2d2ebf235f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create device agnostic code\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TGoZCzOrHC8H",
    "outputId": "0f8d1d8e-e578-4bf6-eea6-1cf27bda4764"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BlobModel(\n",
       "  (linear_layer_stack): Sequential(\n",
       "    (0): Linear(in_features=2, out_features=8, bias=True)\n",
       "    (1): Linear(in_features=8, out_features=8, bias=True)\n",
       "    (2): Linear(in_features=8, out_features=4, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch import nn\n",
    "\n",
    "# Build model\n",
    "class BlobModel(nn.Module):\n",
    "    def __init__(self, input_features, output_features, hidden_units=8):\n",
    "        \"\"\"Initializes all required hyperparameters for a multi-class classification model.\n",
    "\n",
    "        Args:\n",
    "            input_features (int): Number of input features to the model.\n",
    "            out_features (int): Number of output features of the model\n",
    "              (how many classes there are).\n",
    "            hidden_units (int): Number of hidden units between layers, default 8.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.linear_layer_stack = nn.Sequential(\n",
    "            nn.Linear(in_features=input_features, out_features=hidden_units),\n",
    "            # nn.ReLU(), # <- does our dataset require non-linear layers? (try uncommenting and see if the results change)\n",
    "            nn.Linear(in_features=hidden_units, out_features=hidden_units),\n",
    "            # nn.ReLU(), # <- does our dataset require non-linear layers? (try uncommenting and see if the results change)\n",
    "            nn.Linear(in_features=hidden_units, out_features=output_features), # how many classes are there?\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.linear_layer_stack(x)\n",
    "\n",
    "# Create an instance of BlobModel and send it to the target device\n",
    "model_4 = BlobModel(input_features=NUM_FEATURES, \n",
    "                    output_features=NUM_CLASSES, \n",
    "                    hidden_units=8).to(device)\n",
    "model_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_eOASXWAHC8H"
   },
   "source": [
    "Excellent! Our multi-class model is ready to go, let's create a loss function and optimizer for it.\n",
    "\n",
    "### 8.3 Creating a loss function and optimizer for a multi-class PyTorch model\n",
    "\n",
    "Since we're working on a multi-class classification problem, we'll use the `nn.CrossEntropyLoss()` method as our loss function.\n",
    "\n",
    "And we'll stick with using SGD with a learning rate of 0.1 for optimizing our `model_4` parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "3ngHLo--HC8H"
   },
   "outputs": [],
   "source": [
    "# Create loss and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model_4.parameters(), \n",
    "                            lr=0.1) # exercise: try changing the learning rate here and seeing what happens to the model's performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orcVVmLzo3gX"
   },
   "source": [
    "### 8.4 Getting prediction probabilities for a multi-class PyTorch model\n",
    "\n",
    "Alright, we've got a loss function and optimizer ready, and we're ready to train our model but before we do let's do a single forward pass with our model to see if it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "--d6YmldpZh_",
    "outputId": "2b1fb56f-cf42-49a3-f1ec-7978cfd68b56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.2711, -0.6494, -1.4740, -0.7044],\n",
       "        [ 0.2210, -1.5439,  0.0420,  1.1531],\n",
       "        [ 2.8698,  0.9143,  3.3169,  1.4027],\n",
       "        [ 1.9576,  0.3125,  2.2244,  1.1324],\n",
       "        [ 0.5458, -1.2381,  0.4441,  1.1804]], device='cuda:0',\n",
       "       grad_fn=<SliceBackward0>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a single forward pass on the data (we'll need to put it to the target device for it to work)\n",
    "model_4(X_blob_train.to(device))[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0fk1K9VlpoPI"
   },
   "source": [
    "What's coming out here?\n",
    "\n",
    "It looks like we get one value per feature of each sample.\n",
    "\n",
    "Let's check the shape to confirm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3W4jnvmWp0OH",
    "outputId": "f515c93d-2c57-43fe-f5ce-d6b6aa20cd0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4]), 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many elements in a single prediction sample?\n",
    "model_4(X_blob_train.to(device))[0].shape, NUM_CLASSES "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NyQSNaSVqBBN"
   },
   "source": [
    "Wonderful, our model is predicting one value for each class that we have.\n",
    "\n",
    "Do you remember what the raw outputs of our model are called?\n",
    "\n",
    "Hint: it rhymes with \"frog splits\" (no animals were harmed in the creation of these materials).\n",
    "\n",
    "If you guessed *logits*, you'd be correct.\n",
    "\n",
    "So right now our model is outputing logits but what if we wanted to figure out exactly which label is was giving the sample?\n",
    "\n",
    "As in, how do we go from `logits -> prediction probabilities -> prediction labels` just like we did with the binary classification problem?\n",
    "\n",
    "That's where the [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function) comes into play.\n",
    "\n",
    "The softmax function calculates the probability of each prediction class being the actual predicted class compared to all other possible classes.\n",
    "\n",
    "If this doesn't make sense, let's see in code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6hU_Wxudrbiq",
    "outputId": "c12e6a9f-c80f-466c-aa5c-27e30cfe9963"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.2549, -0.8112, -1.4795, -0.5696],\n",
      "        [ 1.7168, -1.2270,  1.7367,  2.1010],\n",
      "        [ 2.2400,  0.7714,  2.6020,  1.0107],\n",
      "        [-0.7993, -0.3723, -0.9138, -0.5388],\n",
      "        [-0.4332, -1.6117, -0.6891,  0.6852]], device='cuda:0',\n",
      "       grad_fn=<SliceBackward0>)\n",
      "tensor([[0.1872, 0.2918, 0.1495, 0.3715],\n",
      "        [0.2824, 0.0149, 0.2881, 0.4147],\n",
      "        [0.3380, 0.0778, 0.4854, 0.0989],\n",
      "        [0.2118, 0.3246, 0.1889, 0.2748],\n",
      "        [0.1945, 0.0598, 0.1506, 0.5951]], device='cuda:0',\n",
      "       grad_fn=<SliceBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# Make prediction logits with model\n",
    "y_logits = model_4(X_blob_test.to(device))\n",
    "\n",
    "# Perform softmax calculation on logits across dimension 1 to get prediction probabilities\n",
    "y_pred_probs = torch.softmax(y_logits, dim=1) \n",
    "print(y_logits[:5])\n",
    "print(y_pred_probs[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A_pbSytSrzHF"
   },
   "source": [
    "Hmm, what's happened here?\n",
    "\n",
    "It may still look like the outputs of the softmax function are jumbled numbers (and they are, since our model hasn't been trained and is predicting using random patterns) but there's a very specific thing different about each sample.\n",
    "\n",
    "After passing the logits through the softmax function, each individual sample now adds to 1 (or very close to).\n",
    "\n",
    "Let's check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5fC5No7IsSiB",
    "outputId": "db517fa9-04eb-4efe-a75b-970bdf2a3163"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1., device='cuda:0', grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the first sample output of the softmax activation function \n",
    "torch.sum(y_pred_probs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhwu9ln1sbl7"
   },
   "source": [
    "These prediction probabilities are essentially saying how much the model *thinks* the target `X` sample (the input) maps to each class.\n",
    "\n",
    "Since there's one value for each class in `y_pred_probs`, the index of the *highest* value is the class the model thinks the specific data sample *most* belongs to.\n",
    "\n",
    "We can check which index has the highest value using `torch.argmax()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6X3Yf5gCsbME",
    "outputId": "a7e4db7e-08fd-426c-8b54-dfd7d3943d79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.1872, 0.2918, 0.1495, 0.3715], device='cuda:0',\n",
      "       grad_fn=<SelectBackward0>)\n",
      "tensor(3, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# Which class does the model think is *most* likely at the index 0 sample?\n",
    "print(y_pred_probs[0])\n",
    "print(torch.argmax(y_pred_probs[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9veE071JtSZJ"
   },
   "source": [
    "You can see the output of `torch.argmax()` returns 3, so for the features (`X`) of the sample at index 0, the model is predicting that the most likely class value (`y`) is 3.\n",
    "\n",
    "Of course, right now this is just random guessing so it's got a 25% chance of being right (since there's four classes). But we can improve those chances by training the model.\n",
    "\n",
    "> **Note:** To summarize the above, a model's raw output is referred to as **logits**.\n",
    "> \n",
    "> For a multi-class classification problem, to turn the logits into **prediction probabilities**, you use the softmax activation function (`torch.softmax`).\n",
    ">\n",
    "> The index of the value with the highest **prediction probability** is the class number the model thinks is *most* likely given the input features for that sample (although this is a prediction, it doesn't mean it will be correct)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hlqJ3_xTupU3"
   },
   "source": [
    "### 8.5 Creating a training and testing loop for a multi-class PyTorch model\n",
    "\n",
    "Alright, now we've got all of the preparation steps out of the way, let's write a training and testing loop to improve and evaluate our model.\n",
    "\n",
    "We've done many of these steps before so much of this will be practice.\n",
    "\n",
    "The only difference is that we'll be adjusting the steps to turn the model outputs (logits) to prediction probabilities (using the softmax activation function) and then to prediction labels (by taking the argmax of the output of the softmax activation function).\n",
    "\n",
    "Let's train the model for `epochs=100` and evaluate it every 10 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "55s1Pis9HC8H",
    "outputId": "4a7b0fd7-8a08-40a4-8694-435178b70832"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 1.04324, Acc: 65.50% | Test Loss: 0.57861, Test Acc: 95.50%\n",
      "Epoch: 10 | Loss: 0.14398, Acc: 99.12% | Test Loss: 0.13037, Test Acc: 99.00%\n",
      "Epoch: 20 | Loss: 0.08062, Acc: 99.12% | Test Loss: 0.07216, Test Acc: 99.50%\n",
      "Epoch: 30 | Loss: 0.05924, Acc: 99.12% | Test Loss: 0.05133, Test Acc: 99.50%\n",
      "Epoch: 40 | Loss: 0.04892, Acc: 99.00% | Test Loss: 0.04098, Test Acc: 99.50%\n",
      "Epoch: 50 | Loss: 0.04295, Acc: 99.00% | Test Loss: 0.03486, Test Acc: 99.50%\n",
      "Epoch: 60 | Loss: 0.03910, Acc: 99.00% | Test Loss: 0.03083, Test Acc: 99.50%\n",
      "Epoch: 70 | Loss: 0.03643, Acc: 99.00% | Test Loss: 0.02799, Test Acc: 99.50%\n",
      "Epoch: 80 | Loss: 0.03448, Acc: 99.00% | Test Loss: 0.02587, Test Acc: 99.50%\n",
      "Epoch: 90 | Loss: 0.03300, Acc: 99.12% | Test Loss: 0.02423, Test Acc: 99.50%\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Set number of epochs\n",
    "epochs = 100\n",
    "\n",
    "# Put data to target device\n",
    "X_blob_train, y_blob_train = X_blob_train.to(device), y_blob_train.to(device)\n",
    "X_blob_test, y_blob_test = X_blob_test.to(device), y_blob_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    model_4.train()\n",
    "\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_4(X_blob_train) # model outputs raw logits \n",
    "    y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # go from logits -> prediction probabilities -> prediction labels\n",
    "    # print(y_logits)\n",
    "    # 2. Calculate loss and accuracy\n",
    "    loss = loss_fn(y_logits, y_blob_train) \n",
    "    acc = accuracy_fn(y_true=y_blob_train,\n",
    "                      y_pred=y_pred)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_4.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_logits = model_4(X_blob_test)\n",
    "      test_pred = torch.softmax(test_logits, dim=1).argmax(dim=1)\n",
    "      # 2. Calculate test loss and accuracy\n",
    "      test_loss = loss_fn(test_logits, y_blob_test)\n",
    "      test_acc = accuracy_fn(y_true=y_blob_test,\n",
    "                             y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Acc: {acc:.2f}% | Test Loss: {test_loss:.5f}, Test Acc: {test_acc:.2f}%\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m_JNlpd4L6dL"
   },
   "source": [
    "### 8.6 Making and evaluating predictions with a PyTorch multi-class model\n",
    "\n",
    "It looks like our trained model is performing pretty well.\n",
    "\n",
    "But to make sure of this, let's make some predictions and visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NjCKKhsGHC8H",
    "outputId": "583e10fb-fa1c-463a-fbd0-4d31d76b82ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[  4.3377,  10.3539, -14.8948,  -9.7642],\n",
       "        [  5.0142, -12.0371,   3.3860,  10.6699],\n",
       "        [ -5.5885, -13.3448,  20.9894,  12.7711],\n",
       "        [  1.8400,   7.5599,  -8.6016,  -6.9942],\n",
       "        [  8.0726,   3.2906, -14.5998,  -3.6186],\n",
       "        [  5.5844, -14.9521,   5.0168,  13.2890],\n",
       "        [ -5.9739, -10.1913,  18.8655,   9.9179],\n",
       "        [  7.0755,  -0.7601,  -9.5531,   0.1736],\n",
       "        [ -5.5918, -18.5990,  25.5309,  17.5799],\n",
       "        [  7.3142,   0.7197, -11.2017,  -1.2011]], device='cuda:0')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "model_4.eval()\n",
    "with torch.inference_mode():\n",
    "    y_logits = model_4(X_blob_test)\n",
    "\n",
    "# View the first 10 predictions\n",
    "y_logits[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lpAdeJRMNHjG"
   },
   "source": [
    "Alright, looks like our model's predictions are still in logit form.\n",
    "\n",
    "Though to evaluate them, they'll have to be in the same form as our labels (`y_blob_test`) which are in integer form.\n",
    "\n",
    "Let's convert our model's prediction logits to prediction probabilities (using `torch.softmax()`) then to prediction labels (by taking the `argmax()` of each sample).\n",
    "\n",
    "> **Note:** It's possible to skip the `torch.softmax()` function and go straight from `predicted logits -> predicted labels` by calling `torch.argmax()` directly on the logits.\n",
    ">\n",
    "> For example, `y_preds = torch.argmax(y_logits, dim=1)`, this saves a computation step (no `torch.softmax()`) but results in no prediction probabilities being available to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "faDQ4oLpHC8H",
    "outputId": "ca32986f-8dc0-419d-84df-1cc3ba8577e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')\n",
      "Labels: tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')\n",
      "Test accuracy: 99.5%\n"
     ]
    }
   ],
   "source": [
    "# Turn predicted logits in prediction probabilities\n",
    "y_pred_probs = torch.softmax(y_logits, dim=1)\n",
    "\n",
    "# Turn prediction probabilities into prediction labels\n",
    "y_preds = y_pred_probs.argmax(dim=1)\n",
    "\n",
    "# Compare first 10 model preds and test labels\n",
    "print(f\"Predictions: {y_preds[:10]}\\nLabels: {y_blob_test[:10]}\")\n",
    "print(f\"Test accuracy: {accuracy_fn(y_true=y_blob_test, y_pred=y_preds)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AMA5SSixOSru"
   },
   "source": [
    "Nice! Our model predictions are now in the same form as our test labels.\n",
    "\n",
    "Let's visualize them with `plot_decision_boundary()`, remember because our data is on the GPU, we'll have to move it to the CPU for use with matplotlib (`plot_decision_boundary()` does this automatically for us)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "kLOzBFdRHC8I",
    "outputId": "71dcaf7f-a30e-457d-8949-916cf9c5cc79"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_4, X_blob_train, y_blob_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_4, X_blob_test, y_blob_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p0vFchUQx7mc"
   },
   "source": [
    "## 9. More classification evaluation metrics\n",
    "\n",
    "So far we've only covered a couple of ways of evaluating a classification model (accuracy, loss and visualizing predictions).\n",
    "\n",
    "These are some of the most common methods you'll come across and are a good starting point.\n",
    "\n",
    "However, you may want to evaluate your classification model using more metrics such as the following:\n",
    "\n",
    "| **Metric name/Evaluation method** | **Defintion** | **Code** |\n",
    "| --- | --- | --- |\n",
    "| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | [`torchmetrics.Accuracy()`](https://torchmetrics.readthedocs.io/en/stable/classification/accuracy.html#id3) or [`sklearn.metrics.accuracy_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) |\n",
    "| Precision | Proportion of true positives over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`torchmetrics.Precision()`](https://torchmetrics.readthedocs.io/en/stable/classification/precision.html#id4) or [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) |\n",
    "| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should've been 1). Higher recall leads to less false negatives. | [`torchmetrics.Recall()`](https://torchmetrics.readthedocs.io/en/stable/classification/recall.html#id5) or [`sklearn.metrics.recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) |\n",
    "| F1-score | Combines precision and recall into one metric. 1 is best, 0 is worst. | [`torchmetrics.F1Score()`](https://torchmetrics.readthedocs.io/en/stable/classification/f1_score.html#f1score) or [`sklearn.metrics.f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) |\n",
    "| [Confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)  | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line). | [`torchmetrics.ConfusionMatrix`](https://torchmetrics.readthedocs.io/en/stable/classification/confusion_matrix.html#confusionmatrix) or [`sklearn.metrics.plot_confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay.from_predictions) |\n",
    "| Classification report | Collection of some of the main classification metrics such as precision, recall and f1-score. | [`sklearn.metrics.classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) |\n",
    "\n",
    "Scikit-Learn (a popular and world-class machine learning library) has many implementations of the above metrics and you're looking for a PyTorch-like version, check out [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/), especially the [TorchMetrics classification section](https://torchmetrics.readthedocs.io/en/stable/pages/classification.html). \n",
    "\n",
    "Let's try the `torchmetrics.Accuracy` metric out.\n",
    "\n",
    "\n"
   ]
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    "try:\n",
    "    from torchmetrics import Accuracy\n",
    "except:\n",
    "    !pip install torchmetrics==0.9.3 # this is the version we're using in this notebook (later versions exist here: https://torchmetrics.readthedocs.io/en/stable/generated/CHANGELOG.html#changelog)\n",
    "    from torchmetrics import Accuracy\n",
    "\n",
    "# Setup metric and make sure it's on the target device\n",
    "torchmetrics_accuracy = Accuracy(task='multiclass', num_classes=4).to(device)\n",
    "\n",
    "# Calculate accuracy\n",
    "torchmetrics_accuracy(y_preds, y_blob_test)"
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    "## Exercises\n",
    "\n",
    "All of the exercises are focused on practicing the code in the sections above.\n",
    "\n",
    "You should be able to complete them by referencing each section or by following the resource(s) linked.\n",
    "\n",
    "All exercises should be completed using [device-agonistic code](https://pytorch.org/docs/stable/notes/cuda.html#device-agnostic-code).\n",
    "\n",
    "Resources:\n",
    "* [Exercise template notebook for 02](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/exercises/02_pytorch_classification_exercises.ipynb)\n",
    "* [Example solutions notebook for 02](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/solutions/02_pytorch_classification_exercise_solutions.ipynb) (try the exercises *before* looking at this)\n",
    "\n",
    "1. Make a binary classification dataset with Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function.\n",
    "    * For consistency, the dataset should have 1000 samples and a `random_state=42`.\n",
    "    * Turn the data into PyTorch tensors. Split the data into training and test sets using `train_test_split` with 80% training and 20% testing.\n",
    "2. Build a model by subclassing `nn.Module` that incorporates non-linear activation functions and is capable of fitting the data you created in 1.\n",
    "    * Feel free to use any combination of PyTorch layers (linear and non-linear) you want.\n",
    "3. Setup a binary classification compatible loss function and optimizer to use when training the model.\n",
    "4. Create a training and testing loop to fit the model you created in 2 to the data you created in 1.\n",
    "    * To measure model accuracy, you can create your own accuracy function or use the accuracy function in [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/).\n",
    "    * Train the model for long enough for it to reach over 96% accuracy.\n",
    "    * The training loop should output progress every 10 epochs of the model's training and test set loss and accuracy.\n",
    "5. Make predictions with your trained model and plot them using the `plot_decision_boundary()` function created in this notebook.\n",
    "6. Replicate the Tanh (hyperbolic tangent) activation function in pure PyTorch.\n",
    "    * Feel free to reference the [ML cheatsheet website](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#tanh) for the formula.\n",
    "7. Create a multi-class dataset using the [spirals data creation function from CS231n](https://cs231n.github.io/neural-networks-case-study/) (see below for the code).\n",
    "    * Construct a model capable of fitting the data (you may need a combination of linear and non-linear layers).\n",
    "    * Build a loss function and optimizer capable of handling multi-class data (optional extension: use the Adam optimizer instead of SGD, you may have to experiment with different values of the learning rate to get it working).\n",
    "    * Make a training and testing loop for the multi-class data and train a model on it to reach over 95% testing accuracy (you can use any accuracy measuring function here that you like).\n",
    "    * Plot the decision boundaries on the spirals dataset from your model predictions, the `plot_decision_boundary()` function should work for this dataset too.\n",
    "\n",
    "```python\n",
    "# Code for creating a spiral dataset from CS231n\n",
    "import numpy as np\n",
    "N = 100 # number of points per class\n",
    "D = 2 # dimensionality\n",
    "K = 3 # number of classes\n",
    "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n",
    "y = np.zeros(N*K, dtype='uint8') # class labels\n",
    "for j in range(K):\n",
    "  ix = range(N*j,N*(j+1))\n",
    "  r = np.linspace(0.0,1,N) # radius\n",
    "  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta\n",
    "  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
    "  y[ix] = j\n",
    "# lets visualize the data\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "## Extra-curriculum\n",
    "\n",
    "* Write down 3 problems where you think machine classification could be useful (these can be anything, get creative as you like, for example, classifying credit card transactions as fraud or not fraud based on the purchase amount and purchase location features). \n",
    "* Research the concept of \"momentum\" in gradient-based optimizers (like SGD or Adam), what does it mean?\n",
    "* Spend 10-minutes reading the [Wikipedia page for different activation functions](https://en.wikipedia.org/wiki/Activation_function#Table_of_activation_functions), how many of these can you line up with [PyTorch's activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity)?\n",
    "* Research when accuracy might be a poor metric to use (hint: read [\"Beyond Accuracy\" by Will Koehrsen](https://willkoehrsen.github.io/statistics/learning/beyond-accuracy-precision-and-recall/) for ideas).\n",
    "* **Watch:** For an idea of what's happening within our neural networks and what they're doing to learn, watch [MIT's Introduction to Deep Learning video](https://youtu.be/7sB052Pz0sQ)."
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